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optimization quantum technologies robotics search and information retrieval security privacy and abuse prevention sustainability our scientific contributions publications research from our scientists and collaborators conferences our experts present and discuss cutting edge research at scientific meetings globally research areas automated reasoning cloud and systems computer vision conversational ai economics information and knowledge management machine learning operations research and optimization quantum technologies robotics search and information retrieval security privacy and abuse prevention sustainability our scientific contributions publications research from our scientists and collaborators conferences our experts present and discuss cutting edge research at scientific meetings globally news blog the latest from amazon researchers amazon science blog technical deep dives and perspectives from our scientists news research milestones and recent achievements the latest from amazon researchers amazon science blog technical deep dives and perspectives from our scientists news research milestones and recent achievements collaborations amazon research awards overview call for proposals latest news research stories recipients amazon nova ai challenge overview rules faqs teams research collaborations overview carnegie mellon university columbia university hampton university howard university iit bombay johns hopkins university max planck society mit tennessee state university university of california los angeles university of illinois urbana champaign university of southern california university of texas at austin virginia tech university of washington amazon research awards overview call for proposals latest news research stories recipients amazon nova ai challenge overview rules faqs teams research collaborations overview carnegie mellon university columbia university hampton university howard university iit bombay johns hopkins university max planck society mit tennessee state university university of california los angeles university of illinois urbana champaign university of southern california university of texas at austin virginia tech university of washington resources code and datasets amazon nova try amazon s frontier foundation models code and datasets amazon nova try amazon s frontier foundation models careers careers explore our open roles amazon scholars faculty research opportunities on industry scale technical challenges postdoctoral science program early career research opportunities alongside experienced industry scientists careers explore our open roles amazon scholars faculty research opportunities on industry scale technical challenges postdoctoral science program early career research opportunities alongside experienced industry scientists search submit search why don t machine learning research agents overfit new research indicates that ai agents learn compressible models of data which don t have enough space to enable memorization read more developing provably correct rust code with verus how the verus program verifier which automatically checks code against a mathematical specification of its functionality helps increase security assurance in software projects read more a decade of mathematical certainty ten years after we founded the automated reasoning group mathematical logic has moved from academic research into production services that secure millions of customer workloads demonstrating that systems can be provably correct not just probably correct read more a new benchmark for evaluating patient facing health ai agents patientagentbench generates a synthetic patient health record a realistic clinical vignette and a patient agent that converses with the ai system under evaluation to capture what a patient facing agent actually has to do read more graviton5 s improved design increases speed and energy efficiency beyond moore s law a new chiplet architecture custom die to die connectivity and support for ddr5 8800 memory and the latest pcie gen6 interconnects improve performance by 25 for general purpose and agentic ai workloads read more amazon news why don t machine learning research agents overfit developing provably correct rust code with verus a decade of mathematical certainty a new benchmark for evaluating patient facing health ai agents graviton5 s improved design increases speed and energy efficiency beyond moore s law customer obsessed science our approach research areas automated reasoning cloud and systems computer vision conversational ai economics information and knowledge management machine learning operations research and optimization quantum technologies robotics search and information retrieval security privacy and abuse prevention sustainability from the blog view all technical deep dives and perspectives from our scientists view all a kernel centric path to real time video generation on trainium september 25 2026 12 min read using the neuron kernel interface a reactor aws collaboration tackled the dynamic shapes memory access patterns and cache management that make real time autoregressive diffusion hard building techniques that generalize across models machine learning advancing ai for biology teaching models to design and characterize antibodies september 21 2026 11 min read machine learning when llm judges agree should we believe them august 26 2026 5 min read conversational ai sop bench a new benchmark for evaluating ai agents on real business procedures august 21 2026 9 min read conversational ai how controllers from industrial machinery can coordinate multitask machine learning july 30 2026 8 min read machine learning view all featured news 34 amazon research awards build on trainium recipients announced the build on trainium program is a 110 million credit initiative supporting ai research at 30 universities including stanford uc berkeley uiuc ucla cmu and mit with a focus on responsible ai aws and hopkins engineering announce database for ai ml antibody design the antibody developability benchmark is powered by one of the most diverse antibody datasets in public literature enabling transparent performance evaluation for ai guided antibody design 2026 amazon nova ai challenge trusted software agents track challenge pushes teams to demonstrate measurable gains in secure coding performance while building ai agents that advance real world utility and reliability at scale amazon launches 68 million ai phd fellowship program initiative will fund over 100 doctoral students researching machine learning computer vision and natural language processing at nine universities publications view all view all espo error structured prompt optimization via diagnose diversify and stabilize lihao liu peng tang kunwar yashraj singh shabnam ghadar emnlp 2026 2026 evolutionary prompt optimizers such as gepa suffer from prompt bloat each iteration appends rules and caveats producing prompts up to 3 longer yet no more accurate we trace this to three deficiencies incomplete error observation limited search diversity and unreliable selection and propose espo error structured prompt optimization which decomposes prompt optimization into three phases diagnose machine learning synthave scalable synthetic labeling for e commerce with llm arena validation andrea scarinci virginia negri brayan impata suleiman khan victor martinez gomez marcello federico emnlp 2026 2026 fine tuning large language models llms for e commerce attribute extraction requires labeled data representative across thousands of product types attributes and multiple languages this combinatorial scale translates to millions of annotations rendering human labeling prohibitively costly while recent work has demonstrated synthetic label generation using llms negri et al 2025 deploying such machine learning failing agents leave traces telemetry signatures for multi tier agent evaluation muthaheera yasmeena emnlp 2026 2026 agent evaluation today depends on per trace llm judge inference or human review too expensive to run on every trace production systems fall back to sampling a fraction we find that failing agents leave a detectable behavioral signature in standard observability telemetry disproportionate effort relative to outcome we formalize four behavioral failure signatures from agent telemetry validated on 4 671 machine learning characterizing and provisioning the environment bubble in agentic rl llm rollout sathishkumar sivashanmugam colm 2026 2026 reinforcement learning rl post training of tool using llm agents leaves expensive rollout gpus idle while each trajectory waits on cpu side environment work sandbox cold start code execution retrieval and external api calls we term this idle the environment bubble and measure it directly on 8 a100 hardware using a 20 hz nvml profiler that avoids the kernel residency pitfall in the utilization counter machine learning validation gated architecture search for self organizing multi agent orchestration amit dhanda sina amini niaki anish acharya brian verkhovsky colm 2026 workshop on lifelong agents 2026 multi agent architectures today are hand designed generated one shot or evolved through unconstrained code search none of which treats the number of agents as a searchable parameter and none of which can discover that a single agent is optimal we argue agent count and coordination topology should be optimized with the same discipline applied to neural architectures bounded edits held out validation machine learning load more collaborations view all whether you re a faculty member or student there are number of ways you can engage with amazon view all gretchen ertl amazon research awards the program offers unrestricted funds and other resources to support research at academic institutions and non profit organizations in areas that align with our mission amazon nova ai challenge a global university competition to drive secure innovation in generative ai technology which focuses on responsible ai and large language model coding security credit wolfram scheible research collaborations we partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest courtesy of pai ling yin amazon scholars we hire world class academics to work on large scale technical challenges while they continue to teach and conduct research at their universities view all work with us see more jobs see more jobs research scientist fauna us ny new york we are seeking a human robot interaction hri research scientist to develop cutting edge interactions that make robots feel alive personal and fun in this role you will focus on verbal and non verbal conversational systems social dynamics memory and long term relationship formation between robots their environments and the people they interact with your contributions will be essential in advancing robotics by enabling expressive socially intelligent and trustworthy interactions between robots and humans research scientist mobile manipulation robotics i o us ma north reading amazon robotics is transforming warehouse automation through edge ai and machine learning applied to real world robotics challenges we re seeking a research scientist to advance our mobile manipulation capabilities by developing novel learning based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments this role offers the opportunity to conduct original research and translate state of the art findings into production systems operating at amazon s unprecedented scale key job responsibilities research and algorithm development formulate novel research problems in robot learning and manipulation design new model architectures validate hypotheses through rigorous experimentation and advance the state of the art in learning based robotics data strategy and pipeline design define data requirements for research initiatives design scalable collection and curation strategies establish governance and provenance standards and build reusable pipelines ensuring data quality and reproducibility experimentation and scientific validation design and execute experiments in simulation and real world embodiments develop evaluation methodologies and benchmarks perform ablation studies and statistical analyses and iterate systematically to advance model performance prototyping and research infrastructure develop clean well documented research codebases build experimentation frameworks and evaluation tooling contribute to shared training infrastructure and implement interfaces for broader robotics integration scientific leadership and publication drive an independent research agenda aligned with team objectives publish at top tier venues e g rss corl icra neurips identify research gaps through literature reviews and present findings via technical reports and talks cross functional collaboration partner with scientists engineers and leaders across teams to translate research into deployable solutions mentor junior researchers contribute to the team s scientific culture and support integration with robotics hardware teams a day in the life if you are not sure that every qualification on the list above describes you exactly we d still love to hear from you at amazon we value people with unique backgrounds experiences and skillsets if you re passionate about this role and want to make an impact on a global scale please apply about the team are you inspired by invention is problem solving through teamwork in your dna do you like the idea of seeing how your work impacts the bigger picture answer yes to any of these and you ll fit right in here at amazon robotics we are a smart collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real world challenges that will transform our customers experiences in ways we can t even image yet we invent new improvements every day we are amazon robotics and we will give you the tools and support you need to invent with us in ways that are rewarding fulfilling and fun applied scientist i amazon shipping in hr gurugram building large scale forecasting and optimization systems that power amazon s global transportation network and directly impact customer experience and cost key job responsibilities 1 guide model and system design across a range of techniques including tree based models deep learning lstms transformers llms and reinforcement learning 2 ensure models are production ready scalable and robust through close partnership with stakeholders 3 partner with product operations and engineering leaders to enable proactive decision making and corrective actions 4 own end to end business metrics directly influencing customer experience cost optimization and network reliability 5 help contribute to the broader ml community through publications conference 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