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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 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 social bluesky threads twitter instagram youtube facebook linkedin github rss menu research 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 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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 publication espo error structured prompt optimization via diagnose diversify and stabilize by lihao liu peng tang kunwar yashraj singh shabnam ghadar 2026 download copy bibtex article liu2026 author lihao liu and peng tang and kunwar yashraj singh and shabnam ghadar title espo error structured prompt optimization via diagnose diversify and stabilize year 2026 url https www amazon science publications espo error structured prompt optimization via diagnose diversify and stabilize share share copy link email x linkedin facebook line reddit qzone sina weibo wechat whatsapp 分享到微信 x download copy bibtex article liu2026 author lihao liu and peng tang and kunwar yashraj singh and shabnam ghadar title espo error structured prompt optimization via diagnose diversify and stabilize year 2026 url https www amazon science publications espo error structured prompt optimization via diagnose diversify and stabilize share share copy link email x linkedin facebook line reddit qzone sina weibo wechat whatsapp 分享到微信 x 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 clusters all training errors into structural patterns in one round propose generates candidates via four complementary strategies with independent biases select applies bootstrap stability selection on seven public nlp benchmarks tweet mmlu gsm8k hotpotqa scone hover and pupa espo improves average accuracy by 3 76 pp over the state of the art 74 67 vs 70 91 for gepa matching or exceeding gepa on every dataset while producing prompts 47 shorter 1 004 vs 1 878 chars and faster at inference cross model experiments across four additional student models gemma 3 12b mistral 14b qwen3 32b claude haiku 4 5 show espo yields the best average accuracy on every model tested with the largest gap on qwen3 gsm8k 15 00 91 40 a generalization bound appendix grounds each phase in a corresponding term of the test time gap and the ablation confirms a key prediction adding diversity without bootstrap selection actually hurts performance 1 20 research areas machine learning tags language models large language models llms natural language processing nlp parameter estimation optimization question answering latest news graph centric agentic intelligence imen grida ben yahya nameet dutia october 1 2026 augmenting a network graph with agentic ai produces a digital twin that can help isolate network failures cloud and systems a kernel centric path to real time video generation on trainium stephen zorio september 25 2026 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 amazon launches research initiative with stanford university to advance ai and science staff writer september 21 2026 the collaboration aims to advance research while broadening participation and translating discovery into real world solutions machine learning work with us see more jobs see more jobs applied scientist manager marketplace intelligence us va arlington the sponsored products and brands team at amazon ads is re imagining the advertising landscape through sota generative ai technologies revolutionizing how millions of customers discover products and engage with brands across amazon com and beyond we are at the forefront of re inventing advertising experiences bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights we are a passionate group of innovators dedicated to developing responsible and intelligent ai technologies that balance the needs of advertisers enhance the shopping experience and strengthen the marketplace if you re energized by solving complex challenges and pushing the boundaries of what s possible with ai join us in shaping the future of advertising the marketplace intelligence mi team is looking for an applied science manager to lead a team of scientists and engineers in building production ml and bandit solutions to customize the search experience we determine which ads to show in amazon search where to place them how many ads to place and to which customers this helps shoppers discover new products while helping advertisers put their products in front of the right customers aligning shoppers advertisers and amazon s interests to do this we apply a broad range of machine learning causal inference and optimization techniques to continuously explore learn and optimize the allocation and ranking of ads on the search page we are an interdisciplinary team with a focus on customer obsession and inventing and simplifying our primary focus is on improving the sp experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them you ll lead the mi interleaving team the interleaving team s mission is to personalize and contextualize sp ad allocation on the search page we do this by modeling shopper responses to the number placement and quality of ads we use online experimentation simulation causal modeling and online feedback to estimate the cost of displacing organic and sponsored content then we incorporate those estimates into ad allocation to deliver an efficient and customized shopping experience for shoppers and improved discoverability and sales for advertisers you ll own the experimentation systems models and online model serving infrastructure to support these solutions this is a unique opportunity for someone who wants to have broad business impact a direct impact on customers and the search experience build scaled real time llm and ml solutions and lead a cross functional team if you are interested in machine learning bandit learning building production systems and leading a team to build these solutions this role is for you we re looking for a leader who can help lay out the vision for the team and grow with it key job responsibilities lead a team of scientists and engineers in building scalable machine learning solutions develop a vision for contextualizing and personalizing sp ads in amazon search create develop and drive a data driven product strategy to define the right quantitative measures of shopper impact using this to evaluate decisions and opportunities tackle and solve challenging science and business problems that balance the interests of advertisers shoppers and amazon own a portfolio of pragmatic long term investments that drive long term growth of the ads and retail businesses develop real time llm and ml algorithms to allocate billions of ads per day in advertising auctions develop efficient algorithms for multi objective optimization and ai control methods to find operating points for the ad marketplace then evolve them develop scientists and ml engineers around machine learning economics and optimization for advertising data scientist ii amz10564056 us ca culver city multiple positions available employer amazon com services llc offered position data scientist ii job location culver city california job number amz10564056 position responsibilities design and implement scalable and reliable approaches to support or automate decision making throughout the business apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear acquire data by building the necessary sql etl queries import processes through various company specific interfaces for accessing oracle redshift and spark storage systems build relationships with stakeholders and counterparts analyze data for trends and input validity by inspecting univariate distributions exploring bivariate relationships constructing appropriate transformations and tracking down the source and meaning of anomalies build models using statistical modeling mathematical modeling econometric modeling network modeling social network modeling natural language processing machine learning algorithms genetic algorithms and neural networks validate models against alternative approaches expected and observed outcome and other business defined key performance indicators implement models that comply with evaluations of the computational demands accuracy and reliability of the relevant etl processes at various stages of production 40 hours week 8 00am 5 00pm salary range 158 681 year to 184 000 year amazon is a total compensation company dependent on the position offered equity sign on payments and other forms of compensation may be provided as part of a total compensation package in addition to a full range of medical financial and or other benefits for more information visit https www aboutamazon com workplace employee benefits amazon com is an equal opportunity affirmative action employer minority female disability veteran gender identity sexual orientation 0000 ai engineer vla model rivr ch zurich rivr an amazon company is building physical ai by deploying autonomous robots for real world doorstep delivery operating daily in diverse urban environments rivr s robots continuously learn from and navigate the millions of scenarios encountered during deliveries by owning the full stack from software our fleet of delivery robots operates globally today generating vast amounts of robotic real world data by utilizing state of the art vision language action vla models large scale generalist models like transformers generative ai and similar methods we can leverage this pool of data to significantly enhance its autonomy navigation and manipulation skills in this role you will develop multi modal models that enable robots to autonomously generate actions from demonstrations real time sensor data and natural language commands we are seeking an expert in vla models imitation learning and generative ai techniques with a deep knowledge of supervised and self supervised learning algorithms if you are passionate about pushing the boundaries of ai we invite you to join us in shaping the future of intelligent robotics key job responsibilities develop and implement vision language action vla models generalist robot transformers and imitation learning algorithms e g diffusion policies to enable robots to autonomously execute complex tasks design test and refine your algorithms to meet the demands of complex real world autonomy and navigation tasks with a focus on spatial reasoning and generalization streamline the data collection and training workflow to efficiently expand model capabilities with new tasks and data sources collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real world data optimize and distill networks for real time deployment on the edge e g nvidia jetson thor build lead and mentor an exceptional team of software engineers...