Careers

Work at the intersection of Generative AI, AI Agents, and large-scale ML, helping build Amazon's world-class advertising business. Key job responsibilities • Lead and contribute to end-to-end ML initiatives with high ambiguity, scale, and complexity from problem formulation through production deployment • Develop and optimize forecasting models by performing hands-on analysis of large-scale datasets to improve ad delivery prediction accuracy and operational efficiency • Build, experiment, and deploy machine learning models through rapid prototyping, rigorous experimentation, and close collaboration with software engineering teams for seamless productization. • Design and execute A/B experiments, collect performance data, and conduct statistical analysis to validate model impact • Establish scalable ML infrastructure including automated pipelines for data processing, model training, validation, and serving • Advance the state of the art by researching innovative machine learning techniques and applying them to forecasting challenges About the team You'll join a highly motivated, collaborative, and entrepreneurial team with a broad mandate to experiment, innovate, and break new ground. Here, your work will directly influence advertiser success and shape the future of programmatic advertising.

Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

Are you inspired by the power of Large Language Models (LLM) to transform the way we interact with technology? Are you fascinated by the use of Generative AI to build an advertiser facing solution that predict problems and coach users while they solve real word problems? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the customer service space? If so, Amazon Advertising's Support Product & Services (SP&S) team has an exciting opportunity for you as an Applied Scientist. Key job responsibilities • Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the advertising support center domain. • Use Transformers and apply other NLP techniques like Sentence embeddings, Dimensionality reduction, clustering and topic modeling to identify customer intents and utterances. • Use services like AWS Lex, AWS Bedrock etc. to develop advertising facing solutions • Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful solutions. • Automating feedback loops for algorithms in production. • Setup and monitor alarms to detect anomalous data patterns and perform root cause analyses to explain and address them. • Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences. A day in the life You will work closely with a cross functional team of Software Engineers, Product Owners, Data Scientists, and Contact Center experts. You will research and investigate the latest options in industry to apply machine learning and generative AI to real world problems. You will work backwards from customer problems and collaborate with stakeholders to determine how to scale new technology and integrate with complicated help channels used by advertisers everyday. About the team SP&S team provides solutions and libraries that are leveraged by teams all across Amazon Advertising to provide timely and personalized help. The team aims to predict Advertisers problems and proactively surface intelligent guidance to customers at the right time. As a AS, you will help the team to achieve its vision of building and implementing the next generation of Contact Center technology. You will build/leverage LLMs to train them on advertising support domain knowledge and work shoulder to shoulder with stakeholders to externalize to users in novel ways.

We are seeking an Applied Science Manager to lead the science vision, research strategy, and execution for customer intent modeling that powers next-generation recommendations and personalization. In this role, you will build and mentor a high-performing team of applied scientists, define the multi-year research roadmap, and deliver production-ready models and systems that improve relevance, discovery, and customer trust at scale. The mandate spans modern recommender-system paradigms such as LLM-enabled personalization, intent and journey understanding, representation learning, generative retrieval/ranking, and agentic/conversational experiences grounded in rigorous experimentation and measurable business impact. Key job responsibilities Own the scientific vision and roadmap for customer intent modeling across the funnel (browse, search, detail-page engagement, add-to-cart, purchase, and post-purchase), translating ambiguous customer problems into a prioritized research and delivery plan. Lead and grow a team of applied scientists, including hiring, mentoring, and building a culture of scientific rigor, innovation, and operational excellence. Drive end-to-end model and system delivery, partnering closely with engineering to design, implement, launch, and operate solutions in high-throughput, low-latency production environments (candidate generation, ranking, re-ranking, and explanation). Advance state-of-the-art personalization using modern techniques (transformers, LLMs, representation learning, reinforcement learning/bandits where appropriate) and ensure research investments translate into measurable lifts via online experiments. Establish an evaluation and experimentation strategy for intent and recommendation quality: offline metrics, counterfactual/off-policy evaluation where applicable, calibrated A/B testing, guardrails (trust, safety, fairness). Build robust intent representations that capture both short-term intent and longer-horizon preferences, with disciplined approaches to privacy, data minimization, and responsible AI Influence product strategy and executive communication, presenting clear scientific narratives, tradeoffs, and decisions to senior leadership and cross-functional stakeholders (product, design, engineering, privacy/legal). Raise the scientific bar via external visibility when appropriate: publications, patents, workshops, and internal scientific reviews while balancing novelty with operational impact.

This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

Amazon created one of the most sophisticated supply chains in the world. From the introduction of Amazon Prime, to the use of advanced technology for package delivery, Amazon consistently drives change from the front of the pack. Amazon is seeking a detail oriented Research Scientist to focus on simulation to assist with process improvement and facility design initiatives in our North American fulfillment network. Successful candidates will be natural self-starters who have the drive to design, model, and simulate new fulfillment center conception and design processes. The Research Scientist will be expected to deeply investigate complex problems and drive relentlessly towards innovative solutions. This role requires collaboration with cross-functional teams to develop and integrate advanced computer vision technologies that detect operational anomalies in real-time, improve equipment reliability, and enhance process efficiency. Key responsibilities include applying computer vision, data modeling, optimization techniques, and advanced analytics (e.g. statistical analysis, regression, DOE) to drive data-informed decisions on processes and designs, integrating new solutions into daily workflows to enable proactive interventions and reduce manual troubleshooting, and demonstrating strong technical expertise, problem-solving skills, and the ability to work effectively across the organization. The ideal candidate will have a track record of delivering impactful, technology-driven solutions through analytical rigor and creative thinking. This role requires a strong passion for customers, a high level of comfort navigating ambiguity, and a keen sense of ownership and drive to deliver results. The ideal candidate will have experience in Science work, business analytics and have the aptitude to incorporate new approaches and methodologies while dealing with ambiguities in sourcing processes. Excellent business and communication skills are a must to develop and define key business questions and to build data sets. You should have demonstrated ability to think strategically and analytically about business, product, and technical challenges. You must be responsive, flexible, and able to succeed within an open collaborative environment. Amazon’s culture encourages innovation and expects to take a high-level of ownership in solving complex problems. Come help us make history! Key job responsibilities - Applying computer vision technologies to detect operational anomalies in real-time, the Research Scientist will be responsible for developing solutions that improve equipment reliability and process efficiency. These innovative capabilities will be integrated into daily workflows to enable proactive interventions and reduce manual troubleshooting, driving measurable improvements across the organization. - Design, develop, and simulate engineering solutions for complex material handling challenges considering human/equipment interactions for the North America fulfillment network - Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. - Analyze historical data to identify trends and support decision making. - Provide requirements to develop analytic capabilities, platforms, and pipelines. - Design, size, and analyze field experiments at scale. - Build decision-making models and propose solution for the business problem you defined. This may include delivery of algorithms to be used in production systems. - Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication. - Utilize code (python or another object oriented language) for data analyzing and modeling algorithm - Develop, document and update simulation standards, including standard results reports - Create basic to highly advanced models and run "what-if" scenarios to help drive to optimal solutions - Analyze historical data to identify trends and support decision making. - Apply statistical or machine learning knowledge to specific business problems and data. - Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. - Provide requirements to develop analytic capabilities, platforms, and pipeline - In-Office 5 Days per week (RTO) - Ability to travel up to 10% A day in the life Amazon Benefits: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan 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 skill sets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The Science & Advance Concepts (SAC) Team is responsible for optimizing material handling operations, enhancing automation, and driving innovation through Simulation/Emulation, Data Integrity/Analysis, and Pilot Development for existing first and middle mile buildings.

We are looking for a Principal Applied Scientist to drive technical innovation in visual reasoning systems across multiple domains. You will be a technical leader who sets the research direction, architects novel solutions, and delivers breakthrough results that advance the state of the art while solving real-world business problems. You will be leading the efforts of building a next-generation visual reasoning engine powered by frontier Large Video Models (LVMs). Your mission is to build a system that rivals human understanding of the physical world — moving far beyond the static perception of detection and tracking into the realm of deep spatial-temporal reasoning. This is not a passive computer vision tool; it is an agentic collaborator capable of interpreting natural language instructions, navigating unstructured environments, and executing complex tasks. You will sit at the high-stakes intersection of LVMs, LLMs, and Agentic AI, engineering systems that don't just 'see' but reason and act within the physical world. You will own end-to-end technical solutions from research to production deployment, driving innovation through hands-on research, prototyping, and deployment while delivering production impact. Key job responsibilities * Direct the technical vision for next-gen visual reasoning, pioneering the use of LVMs to solve high-dimensional spatial-temporal problems * Designing and implementing novel algorithms that push the boundaries of what's possible with generative AI * Architecting scalable solutions that deliver real-time insights across diverse environments * Building agentic AI systems that autonomously execute end-to-end workflows, transforming visual data into actionable business intelligence * Leading cross-functional collaboration to translate research breakthroughs into production systems * Publishing research at top-tier conferences (CVPR, NeurIPS, ICML) and establishing technical thought leadership in visual reasoning and multi-modal AI * Mentoring scientists and engineers to elevate technical excellence across the organization * Influencing product roadmaps through deep technical expertise and business acumen About the team Just Walk Out (JWO) is a checkout-free shopping experience where customers simply enter the store, take what they want, and leave—no lines, no scanning, no checkout. Our Just Walk Out Technology automatically detects when products are taken from or returned to the shelves, keeps track of them in a virtual cart, and charges customers' accounts after they leave. Check it out at https://www.justwalkout.com/. Designed and custom-built by Amazonians, our Just Walk Out Technology uses cutting-edge visual reasoning systems powered by vision-language-reasoning models to understand complex shopping behaviors in real-time. Our algorithms process multi-camera video streams to track customers throughout their shopping journey and determine exactly what items customers take—all without requiring them to scan or checkout. Innovation is part of our DNA! We are now applying our visual reasoning expertise to solve critical challenges in new domains beyond retail. We need people who want to join an ambitious program to expand beyond retail, building state-of-the-art visual reasoning systems that work across domains in physical AI. This expansion represents a significant opportunity to apply cutting-edge vision-language models, multi-modal AI, and generative AI technologies to enterprise applications with massive business impact, enabling automated decision-making capabilities.

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. - Perform data analysis and build data pipelines to drive business decisions. - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. - Review and guide science solutions across the team. - Publish and socialize your and the team's research across Amazon and external avenues as appropriate - Leverage industry best practices to establish repeatable applied science practices, principles & processes.

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities - Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. - Perform data analysis and build data pipelines to drive business decisions. - Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. - Review and guide science solutions across the team. - Publish and socialize your and the team's research across Amazon and external avenues as appropriate - Leverage industry best practices to establish repeatable applied science practices, principles & processes.