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.

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.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Research Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing causal models and randomized controlled trials to assess marketing effectiveness and inform strategic decision-making. This role is suitable for candidates with strong background in causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal Models Design, build, and validate causal models to evaluate the impact of marketing campaigns and initiatives. Leverage advanced statistical methods to identify and quantify causal relationships. Conduct Randomized Controlled Trials Design and implement randomized controlled trials (RCTs) to rigorously test the effectiveness of marketing strategies. Ensure robust experimental design and proper execution to derive credible insights. Statistical Analysis and Inference Perform complex statistical analyses to interpret data from experiments and observational studies. Use statistical software and programming languages to analyze large datasets and extract meaningful patterns. Data-Driven Decision Making Collaborate with marketing teams to provide data-driven recommendations that enhance campaign performance and ROI. Present findings and insights to stakeholders in a clear and actionable manner. Collaborative Problem Solving Work closely with cross-functional teams, including marketing, product, and engineering, to identify key business questions and develop analytical solutions. Foster a culture of data-informed decision-making across the organization. Stay Current with Industry Trends Keep abreast of the latest developments in data science, causal inference, and marketing analytics. Apply new methodologies and technologies to improve the accuracy and efficiency of marketing measurement. Documentation and Reporting Maintain comprehensive documentation of models, experiments, and analytical processes. Prepare reports and presentations that effectively communicate complex analyses to non-technical audiences.

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.

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.

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.

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.

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.