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<title>Dynamo: Amazon’s highly available key-value store - Amazon Science</title><link rel="canonical" href="https://www.amazon.science/publications/dynamo-amazons-highly-available-key-value-store"><meta name="brightspot.contentId" content="0000017e-82c9-de9e-a7ff-c7df71830000">
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<h1 class="PublicationDetailPage-headline">
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Dynamo: Amazon’s highly available key-value store
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<span class="PublicationDetailPage-authorBy">By </span><span class="Link">Giuseppe DeCandia</span>, <span class="Link">Deniz Hastorun</span>, <a class="Link" href="https://www.amazon.science/author/madan-jampani" data-cms-ai="0" >Madan Jampani</a>, <span class="Link">Gunavardhan Kakulapati</span>, <span class="Link">Avinash Lakshman</span>, <span class="Link">Alex Pilchin</span>, <a class="Link" href="https://www.amazon.science/author/swaminathan-sivasubramanian" data-cms-ai="0" >Swaminathan Sivasubramanian</a>, <span class="Link">Peter Vosshall</span>, <a class="Link" href="https://www.amazon.science/author/werner-vogels" data-cms-ai="0" >Werner Vogels </a></div>
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@Article{DeCandia2007,
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author = {Giuseppe DeCandia and Deniz Hastorun and Madan Jampani and Gunavardhan Kakulapati and Avinash Lakshman and Alex Pilchin and Swaminathan Sivasubramanian and Peter Vosshall and Werner Vogels },
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title = {Dynamo: Amazon’s highly available key-value store},
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year = {2007},
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url = {https://www.amazon.science/publications/dynamo-amazons-highly-available-key-value-store},
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@Article{DeCandia2007,
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author = {Giuseppe DeCandia and Deniz Hastorun and Madan Jampani and Gunavardhan Kakulapati and Avinash Lakshman and Alex Pilchin and Swaminathan Sivasubramanian and Peter Vosshall and Werner Vogels },
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title = {Dynamo: Amazon’s highly available key-value store},
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Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face <br>of these failures drives the reliability and scalability of the software systems.<br><br>This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon’s core services use to provide an “always-on” experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
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<a class="Link" aria-label="Making LLMs faster without sacrificing accuracy" href="https://www.amazon.science/blog/making-llms-faster-without-sacrificing-accuracy" data-cms-ai="0" ><picture><source type="image/webp" width="1240" height="695" data-image-size="promoMediumXL" data-srcset="https://cdn.amazon.science/dims4/default/09fac16/2147483647/strip/true/crop/1247x699+91+0/resize/320x179!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 320w,https://cdn.amazon.science/dims4/default/04cba5f/2147483647/strip/true/crop/1247x699+91+0/resize/480x269!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 480w,https://cdn.amazon.science/dims4/default/e35a842/2147483647/strip/true/crop/1247x699+91+0/resize/600x336!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 600w,https://cdn.amazon.science/dims4/default/3cd8ddd/2147483647/strip/true/crop/1247x699+91+0/resize/768x430!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 768w,https://cdn.amazon.science/dims4/default/4df76b5/2147483647/strip/true/crop/1247x699+91+0/resize/1240x695!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 1240w,https://cdn.amazon.science/dims4/default/7f8986a/2147483647/strip/true/crop/1247x699+91+0/resize/2480x1390!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 2480w"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+'/><source width="1240" height="695" data-image-size="promoMediumXL" data-srcset="https://cdn.amazon.science/dims4/default/92ff30e/2147483647/strip/true/crop/1247x699+91+0/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+'/>
|
||
|
||
<img class="Image" data-image-size="promoMediumXL" alt="Throughput and predicted training loss .png" srcset="https://cdn.amazon.science/dims4/default/ad92d8c/2147483647/strip/true/crop/1247x699+91+0/resize/320x179!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 320w,https://cdn.amazon.science/dims4/default/d25453c/2147483647/strip/true/crop/1247x699+91+0/resize/480x269!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 480w,https://cdn.amazon.science/dims4/default/c06bde0/2147483647/strip/true/crop/1247x699+91+0/resize/600x336!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 600w,https://cdn.amazon.science/dims4/default/817abd4/2147483647/strip/true/crop/1247x699+91+0/resize/768x430!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 768w,https://cdn.amazon.science/dims4/default/92ff30e/2147483647/strip/true/crop/1247x699+91+0/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 1240w,https://cdn.amazon.science/dims4/default/0bfa659/2147483647/strip/true/crop/1247x699+91+0/resize/2480x1390!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png 2480w" width="1240" height="695" data-src="https://cdn.amazon.science/dims4/default/92ff30e/2147483647/strip/true/crop/1247x699+91+0/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F06%2F10%2F33372ecf4a939d84a16e09e42d99%2Fthroughput-and-predicted-training-loss.png" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+">
|
||
</picture>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
<div class="PromoA-content">
|
||
|
||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.science/blog/making-llms-faster-without-sacrificing-accuracy" data-cms-ai="0" >Making LLMs faster without sacrificing accuracy</a>
|
||
</div>
|
||
|
||
|
||
<div class="PromoA-details">
|
||
|
||
|
||
|
||
<div class="PromoA-authors"><a class="Link" href="https://www.amazon.science/author/tao-yu" data-cms-ai="0" >Tao Yu</a>, <a class="Link" href="https://www.amazon.science/author/youngsuk-park" data-cms-ai="0" >Youngsuk Park</a></div>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<div class="PromoA-date">May 15, 2026</div>
|
||
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
<div class="PromoA-description">A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.</div>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<div class="PromoA-category">
|
||
<a class="Link" href="https://www.amazon.science/research-areas/conversational-ai-natural-language-processing" data-cms-ai="0" >Conversational AI</a>
|
||
</div>
|
||
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
</div>
|
||
|
||
</li>
|
||
|
||
<li class="ListB-items-item">
|
||
<div class="PromoA" data-content-type="blog post"
|
||
|
||
data-image-align="top" >
|
||
|
||
<div class="PromoA-media">
|
||
|
||
|
||
|
||
<a class="Link" aria-label="Promptimus: Improving already good LLM prompts with zero manual engineering" href="https://www.amazon.science/blog/promptimus-improving-already-good-llm-prompts-with-zero-manual-engineering" data-cms-ai="0" ><picture><source type="image/webp" width="1240" height="695" data-image-size="promoMediumXL" data-srcset="https://cdn.amazon.science/dims4/default/4f0611d/2147483647/strip/true/crop/1920x1076+0+2/resize/320x179!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 320w,https://cdn.amazon.science/dims4/default/4f1c348/2147483647/strip/true/crop/1920x1076+0+2/resize/480x269!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 480w,https://cdn.amazon.science/dims4/default/643f67a/2147483647/strip/true/crop/1920x1076+0+2/resize/600x336!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 600w,https://cdn.amazon.science/dims4/default/17c2aa5/2147483647/strip/true/crop/1920x1076+0+2/resize/768x430!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 768w,https://cdn.amazon.science/dims4/default/a433b8e/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 1240w,https://cdn.amazon.science/dims4/default/e3f45e9/2147483647/strip/true/crop/1920x1076+0+2/resize/2480x1390!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 2480w"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+'/><source width="1240" height="695" data-image-size="promoMediumXL" data-srcset="https://cdn.amazon.science/dims4/default/b06de92/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+'/>
|
||
|
||
<img class="Image" data-image-size="promoMediumXL" alt="Promptimus-02b-16x9.png" srcset="https://cdn.amazon.science/dims4/default/cc9ef99/2147483647/strip/true/crop/1920x1076+0+2/resize/320x179!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 320w,https://cdn.amazon.science/dims4/default/37a4b29/2147483647/strip/true/crop/1920x1076+0+2/resize/480x269!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 480w,https://cdn.amazon.science/dims4/default/9aa77a7/2147483647/strip/true/crop/1920x1076+0+2/resize/600x336!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 600w,https://cdn.amazon.science/dims4/default/cf0587c/2147483647/strip/true/crop/1920x1076+0+2/resize/768x430!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 768w,https://cdn.amazon.science/dims4/default/b06de92/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 1240w,https://cdn.amazon.science/dims4/default/3e29ed4/2147483647/strip/true/crop/1920x1076+0+2/resize/2480x1390!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png 2480w" width="1240" height="695" data-src="https://cdn.amazon.science/dims4/default/b06de92/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F71%2F43%2Fed92ff934c64892903a6cc1c9f39%2Fpromptimus-02b-16x9.png" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+">
|
||
</picture>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
<div class="PromoA-content">
|
||
|
||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.science/blog/promptimus-improving-already-good-llm-prompts-with-zero-manual-engineering" data-cms-ai="0" >Promptimus: Improving already good LLM prompts with zero manual engineering</a>
|
||
</div>
|
||
|
||
|
||
<div class="PromoA-details">
|
||
|
||
|
||
|
||
<div class="PromoA-authors"><a class="Link" href="https://www.amazon.science/author/zhengyuan-shen" data-cms-ai="0" >Zhengyuan Shen</a>, <a class="Link" href="https://www.amazon.science/author/yunfei-bai" data-cms-ai="0" >Yunfei Bai</a>, <a class="Link" href="https://www.amazon.science/author/sullam-jeoung" data-cms-ai="0" >Sullam Jeoung</a>, <a class="Link" href="https://www.amazon.science/author/shuai-wang" data-cms-ai="0" >Shuai Wang</a></div>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<div class="PromoA-date">May 14, 2026</div>
|
||
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
<div class="PromoA-description">By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality.</div>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
</div>
|
||
|
||
|
||
</div>
|
||
|
||
</li>
|
||
|
||
<li class="ListB-items-item">
|
||
<div class="PromoA" data-content-type="blog post"
|
||
|
||
data-image-align="top" >
|
||
|
||
<div class="PromoA-media">
|
||
|
||
|
||
|
||
<a class="Link" aria-label="Navigating uncertainty in Amazon's middle-mile network" href="https://www.amazon.science/blog/navigating-uncertainty-in-amazons-middle-mile-network" data-cms-ai="0" ><picture><source type="image/webp" width="1240" height="695" data-image-size="promoMediumXL" data-srcset="https://cdn.amazon.science/dims4/default/721f093/2147483647/strip/true/crop/1920x1076+0+2/resize/320x179!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 320w,https://cdn.amazon.science/dims4/default/3f9ffa4/2147483647/strip/true/crop/1920x1076+0+2/resize/480x269!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 480w,https://cdn.amazon.science/dims4/default/95b1c48/2147483647/strip/true/crop/1920x1076+0+2/resize/600x336!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 600w,https://cdn.amazon.science/dims4/default/40b64f6/2147483647/strip/true/crop/1920x1076+0+2/resize/768x430!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 768w,https://cdn.amazon.science/dims4/default/cb3377e/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 1240w,https://cdn.amazon.science/dims4/default/2602a36/2147483647/strip/true/crop/1920x1076+0+2/resize/2480x1390!/format/webp/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 2480w"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+'/><source width="1240" height="695" data-image-size="promoMediumXL" data-srcset="https://cdn.amazon.science/dims4/default/96b90a2/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+'/>
|
||
|
||
<img class="Image" data-image-size="promoMediumXL" alt="MiddleMile-02-16x9 (1).png" srcset="https://cdn.amazon.science/dims4/default/9f607f4/2147483647/strip/true/crop/1920x1076+0+2/resize/320x179!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 320w,https://cdn.amazon.science/dims4/default/ddc3b51/2147483647/strip/true/crop/1920x1076+0+2/resize/480x269!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 480w,https://cdn.amazon.science/dims4/default/0b4e1a6/2147483647/strip/true/crop/1920x1076+0+2/resize/600x336!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 600w,https://cdn.amazon.science/dims4/default/885512a/2147483647/strip/true/crop/1920x1076+0+2/resize/768x430!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 768w,https://cdn.amazon.science/dims4/default/96b90a2/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 1240w,https://cdn.amazon.science/dims4/default/a0d1945/2147483647/strip/true/crop/1920x1076+0+2/resize/2480x1390!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png 2480w" width="1240" height="695" data-src="https://cdn.amazon.science/dims4/default/96b90a2/2147483647/strip/true/crop/1920x1076+0+2/resize/1240x695!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa1%2F1b%2Fbe6fc3b44d6986a6f0706260ab82%2Fmiddlemile-02-16x9-1.png" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+">
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</a>
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</div>
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<div class="PromoA-content">
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|
||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.science/blog/navigating-uncertainty-in-amazons-middle-mile-network" data-cms-ai="0" >Navigating uncertainty in Amazon's middle-mile network</a>
|
||
</div>
|
||
|
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|
||
<div class="PromoA-details">
|
||
|
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|
||
<div class="PromoA-authors"><a class="Link" href="https://www.amazon.science/author/ruth-misener" data-cms-ai="0" >Ruth Misener</a>, <a class="Link" href="https://www.amazon.science/author/hana-ku" data-cms-ai="0" >Hana Ku</a>, <a class="Link" href="https://www.amazon.science/author/georgios-paschos" data-cms-ai="0" >Georgios Paschos</a></div>
|
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|
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<div class="PromoA-date">May 6, 2026</div>
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</div>
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|
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<div class="PromoA-description">Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.</div>
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<div class="PromoA-category">
|
||
<a class="Link" href="https://www.amazon.science/research-areas/operations-research-and-optimization" data-cms-ai="0" >Operations research and optimization</a>
|
||
</div>
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<h2 class="ListI-header">Work with us</h2><a class="ListI-header-button" href="https://www.amazon.science/careers" data-cms-ai="0">See more jobs</a>
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data-image-align="top" >
|
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|
||
<div class="PromoA-content">
|
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|
||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.jobs/jobs/10423171/roboticsai-motor-control-scientist-fauna?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Robotics/AI Motor Control Scientist, Fauna</a>
|
||
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<div class="PromoA-details">
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<div class="PromoA-location">US, NY, New York</div>
|
||
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||
</div>
|
||
|
||
|
||
<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.</div>
|
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<div class="PromoA" data-no-media
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data-image-align="top" >
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<div class="PromoA-content">
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||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.jobs/jobs/10423170/senior-applied-scientist-fauna?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Senior Applied Scientist, Fauna</a>
|
||
</div>
|
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<div class="PromoA-details">
|
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||
<div class="PromoA-location">US, NY, New York</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.</div>
|
||
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<div class="PromoA" data-no-media
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data-image-align="top" >
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<div class="PromoA-content">
|
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|
||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.jobs/jobs/10423168/roboticsai-motor-control-scientist-fauna?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Robotics/AI Motor Control Scientist, Fauna</a>
|
||
</div>
|
||
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<div class="PromoA-details">
|
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|
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<div class="PromoA-location">US, NY, New York</div>
|
||
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||
</div>
|
||
|
||
|
||
<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.</div>
|
||
|
||
|
||
|
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|
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<div class="ListI-slide">
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<div class="PromoA" data-no-media
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data-image-align="top" >
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<div class="PromoA-content">
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||
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||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.jobs/jobs/10423323/-applied-science-intern-computer-vision-amazon-international-machine-learning?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >2027 Applied Science Intern (Computer Vision), Amazon International Machine Learning</a>
|
||
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|
||
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||
|
||
<div class="PromoA-details">
|
||
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<div class="PromoA-location">AU, VIC, Melbourne</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Are you excited about leveraging state-of-the-art Computer Vision algorithms and large datasets to solve real-world problems? Join Amazon as an Applied Scientist Intern and be at the forefront of AI innovation! As an Applied Scientist Intern, you'll work in a fast-paced, cross-disciplinary team of pioneering researchers. You'll tackle complex problems, developing solutions that either build on existing academic and industrial research or stem from your own innovative thinking. Your work may even find its way into customer-facing products, making a real-world impact. Key job responsibilities - Develop novel solutions and build prototypes - Work on complex problems in Computer Vision and Machine Learning - Contribute to research that could significantly impact Amazon's operations - Collaborate with a diverse team of experts in a fast-paced environment - Collaborate with scientists on writing and submitting papers to Tier-1 conferences (e.g., CVPR, ICCV, NeurIPS, ICML) - Present your research findings to both technical and non-technical audiences Key Opportunities: - Collaborate with leading machine learning researchers - Access innovative tools and hardware (large GPU clusters) - Address challenges at an unparalleled scale - Become a disruptor, innovator, and problem solver in the field of computer vision - Potentially deliver solutions to production in customer-facing applications - Opportunities to become an FTE after the internship Join us in shaping the future of AI at Amazon. Apply now and turn your research into real-world solutions!</div>
|
||
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||
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|
||
<a class="Link" href="https://www.amazon.jobs/jobs/10423056/economist-genai-economics-and-analytics?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Economist, GenAI Economics and Analytics</a>
|
||
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|
||
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|
||
<div class="PromoA-details">
|
||
|
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||
<div class="PromoA-location">US, WA, Seattle</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">The Alexa for Shopping team is seeking a customer-obsessed senior economist to own and drive analytics strategy for GenAI-powered Shopping experiences. This role will partner closely with senior leaders to deliver high-quality insights that inform executive decision-making for the AI shopping assistant, Rufus. The successful candidate will demonstrate strong attention to detail, excellent written and verbal communication, and the ability to influence across organizations. In this role, you will mentor and set the bar for data science, economics, and engineering partners by establishing best practices for understanding customer behavior in AI-driven shopping experiences. You will invent and scale metrics that measure customer adoption and habituation, and build agentic, automated analytical workflows that enable fast, repeatable deep dives. This position will play a critical role in shaping product roadmap and investment decisions in a rapidly evolving GenAI space. The ideal candidate will operate effectively in ambiguous environments, exercise strong business judgment on high-impact, one-way door decisions, and continuously raise the bar for analytical rigor and operational excellence. You will work cross-functionally with product, engineering, and economics partners to deliver results for customers Key job responsibilities - Own the development of customer and shopping-mission cohorts to understand behavior with and without Rufus engagement across the end-to-end shopping journey. - Identify which Rufus query types and interaction patterns drive the most customer value for specific customer cohorts and shopping missions. - Build predictive models to estimate customer re-engagement and long-term adoption of Rufus based on interaction quality and downstream shopping outcomes. - Invent, operationalize, and publish scalable metrics and dashboards that surface actionable insights, enabling data-driven product growth and executive decision-making. - Partner closely with Product, Engineering, and Economics teams to translate analytical insights into roadmap priorities and customer-focused improvements. About the team The Alexa for Shopping economics team focuses on understanding how GenAI-powered shopping tools are transforming customer behavior across the shopping lifecycle - from inspiration and problem-solving, to product research, selection, purchase, and post-purchase support. We build the foundational measurement frameworks that enable teams to evaluate performance, identify what Rufus experiences resonate most with customers, and uncover opportunities for improvement. Our work directly influences customer-centric product roadmap decisions and helps scale impactful, high-quality AI shopping experiences</div>
|
||
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||
|
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|
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|
||
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|
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|
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|
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|
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<div class="ListI-slide">
|
||
<div class="PromoA" data-no-media
|
||
|
||
data-image-align="top" >
|
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|
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|
||
<div class="PromoA-content">
|
||
|
||
<div class="PromoA-title">
|
||
<a class="Link" href="https://www.amazon.jobs/jobs/10422560/member-of-technical-staff--science-frontier-ai--robotics-far?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Member of Technical Staff - Science, Frontier AI & Robotics (FAR)</a>
|
||
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|
||
|
||
|
||
<div class="PromoA-details">
|
||
|
||
|
||
|
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|
||
<div class="PromoA-location">US, CA, San Francisco</div>
|
||
|
||
</div>
|
||
|
||
|
||
<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Amazon’s Frontier AI & Robotics (FAR) team is seeking a Member of Technical Staff to drive foundational research and build intelligent robotic systems from the ground up. In this role, you will operate at the intersection of cutting-edge AI research and real-world robotics - conducting original research, publishing, and deploying your innovations into production systems at Amazon scale. We’re looking for researchers who think from first principles, push the boundaries of what’s possible, and take full ownership of turning breakthrough ideas into working systems. You will join the next revolution in robotics, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence and independent research initiatives in areas such as locomotion, manipulation, perception, sim2real transfer, multi-modal, multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You’ll have the freedom to pursue ambitious research directions while leveraging Amazon’s vast computational resources to tackle ambiguous problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robot co-design, dexterous manipulation mechanisms, innovative actuation strategies, state estimation, low-level control, system identification, reinforcement learning, sim-to-real transfer, as well as foundation models focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to team's technical decisions and influence implementation strategies to help shape our approach to next-generation robotics challenges - Mentor fellow researchers while maintaining solid individual technical contributions A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges across the full robotics stack - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions and brainstorming sessions with team leaders, fellow researchers and key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster and extensive robotics infrastructure - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.</div>
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<a class="Link" href="https://www.amazon.jobs/jobs/10422776/member-of-technical-staff--hardware-science-frontier-ai--robotics-far?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Member of Technical Staff - Hardware Science, Frontier AI & Robotics (FAR)</a>
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<div class="PromoA-location">US, CA, San Francisco</div>
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<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Amazon’s Frontier AI & Robotics (FAR) team is seeking a Member of Technical Staff to drive foundational research and build intelligent robotic systems from the ground up. In this role, you will operate at the intersection of cutting-edge AI research and real-world robotics - conducting original research, publishing, and deploying your innovations into production systems at Amazon scale. We’re looking for researchers who think from first principles, push the boundaries of what’s possible, and take full ownership of turning breakthrough ideas into working systems. You will join the next revolution in robotics, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence and independent research initiatives in areas such as locomotion, manipulation, perception, sim2real transfer, multi-modal, multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You’ll have the freedom to pursue ambitious research directions while leveraging Amazon’s vast computational resources to tackle ambiguous problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, driving breakthrough approaches through hands-on research and development in areas including robot co-design, dexterous manipulation mechanisms, innovative actuation strategies, state estimation, low-level control, system identification, reinforcement learning, sim-to-real transfer, as well as foundation models focusing on breakthrough approaches in perception, and manipulation. - Lead and Guide technical direction for full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development - Develop and optimize control algorithms and sensing pipelines that enable robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to team's technical decisions and influence implementation strategies to help shape our approach to next-generation robotics challenges - Mentor fellow researchers while maintaining solid individual technical contributions A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges across the full robotics stack - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions and brainstorming sessions with team leaders, fellow researchers and key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster and extensive robotics infrastructure - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.</div>
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<a class="Link" href="https://www.amazon.jobs/jobs/10421937/applied-scientist-i?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist I</a>
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<div class="PromoA-location">IN, KA, Bengaluru</div>
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<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Amazon is looking for a passionate, talented, and inventive Applied Scientists with machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV). Key job responsibilities Amazon is looking for a passionate, talented, and inventive Applied Scientists with machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding. We are hiring in all areas of human language technology: ASR, MT, NLU, text-to-speech (TTS), and Dialog Management, in addition to Computer Vision. We are also looking for talents with experiences/expertise in building large-scale, high-performing systems. A day in the life 0</div>
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<a class="Link" href="https://www.amazon.jobs/jobs/10422061/applied-scientist-central-machine-learning?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist, Central Machine Learning</a>
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<div class="PromoA-location">IN, KA, Bengaluru</div>
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<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the International Emerging Stores organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team Central Machine Learning team works closely with the IES business and engineering teams in building ML solutions that create an impact for Emerging Marketplaces. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on millions of consumers and end users.</div>
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<a class="Link" href="https://www.amazon.jobs/jobs/10421662/sr-data-scientist-enterprise-security-products?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Sr. Data Scientist, Enterprise Security Products</a>
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<div class="PromoA-location">US, TX, Austin</div>
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<div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI — at the same time. This role is a chance to shape the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale. We're looking for a Data Scientist who thrives at the intersection of applied ML, agentic AI, and security. You'll design and deploy models that detect threats, power intelligent agents, and make security decisions at cloud scale. You'll work shoulder-to-shoulder with SDEs, applied scientists, security researchers, and PMs on a team where the best idea wins, regardless of title or tenure. Key job responsibilities * Build the intelligence behind AI-first security products: Design, train, and ship ML models that power agentic systems, anomaly detection, threat classification, and automated response — all running across multi-cloud environments. * Own the full science lifecycle: From problem framing and data exploration through model development, evaluation, production deployment, and monitoring. You build it, you ship it, you run it. * Build with AI to build AI: Use agentic coding tools, LLM-powered workflows, and experimental AI tooling to accelerate every phase of your work; from EDA to feature engineering to model iteration. Multiply your velocity and raise the bar for what one scientist can deliver. * Power agentic architectures: Develop the models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready. * Prototype rapidly and validate with customers: Turn hypotheses into prototypes in days, not quarters. Iterate based on real customer signal and ship what works. * Partner across disciplines: Work directly with SDEs, applied scientists, security researchers, PMs, and UX designers to turn ambiguous problems into shipped solutions. Small team means short lines between you and the decision. * Communicate with impact: Translate complex modeling results into clear recommendations for engineers, product leaders, and senior executives. Influence direction with data. * Raise the science bar: Contribute to technical and science reviews, mentor teammates, and champion AI-first development practices. Help shape the science culture of a fast-growing team from the ground floor. A day in the life No two days look the same on this fast-growing, AI-first team. You might start your morning reviewing evaluation results from overnight model training runs, then dive into building a RAG pipeline or tuning a multi-agent orchestration loop. Before lunch, you're pair-prompting with an agentic coding assistant to stand up a new feature pipeline. In the afternoon, you join a design session with senior and principal scientists and engineers where your ideas carry weight regardless of title. You own science problems end to end, ship using the latest AI-assisted workflows, and see your models reach production fast. This is where builders thrive. About the team Amazon Enterprise Security Products is built by builders who tackle challenges others might consider too ambitious. We're a small team where there are no layers between you and the decision, no waiting quarters to see your work reach customers. Every team member brings an owner's mentality. If there's a problem worth solving, we solve it. No mission is beyond reach, no detail beneath our attention. We move fast, we ship fast, and we learn from what we ship. This is where builders who want to make the impossible routine come to do their best work. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.</div>
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