Careers

Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement methods for dexterous manipulation - Design and implement methods for use of dexterous end effectors with force and tactile sensing - Develop a hierarchical system that combines low-level control with high-level planning - Utilize state-of-the-art manipulation models and optimal control techniques

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.

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're a new research lab based in San Francisco and Boston focused on developing foundational capabilities for useful AI agents. We're pursuing several key research bets that will enable AI agents to perform real-world actions, learn from human feedback, self-course-correct, and infer human goals. We're particularly excited about combining large language models (LLMs) with reinforcement learning (RL) to solve reasoning and planning, learned world models, and generalizing agents to physical environments. We're a small, talent-dense team with the resources and scale of Amazon. Each team has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. AI agents are the next frontier—the right research bets can reinvent what's possible. Join us and help build this lab from the ground up. Key job responsibilities * Define the product vision and roadmap for our agentic developer platform, translating research into products developers love * Partner deeply with research and engineering to identify which capabilities are ready for productization and shape how they're exposed to customers * Own the developer experience end-to-end from API design and SDK ergonomics to documentation, sample apps, and onboarding flows * Understand our customers deeply by engaging directly with developers and end-users, synthesizing feedback, and using data to drive prioritization * Shape how the world builds AI agents by defining new primitives, patterns, and best practices for agentic applications About the team Our team brings the AGI Lab's agent capabilities to customers. We build accessible, usable products: interfaces, frameworks, and solutions, that turn our platform and model capabilities into AI agents developers can use. We own the Nova Act agent playground, Nova Act IDE extension, Nova Act SDK, Nova Act AWS Console, reference architectures, sample applications, and more.

Do you want to join an innovative team of scientists who leverage machine learning and statistical techniques to revolutionize how businesses discover and purchase products on Amazon? Are you passionate about building intelligent systems that understand and predict complex B2B customer needs? The Amazon Business team is looking for exceptional Applied Science to help shape the future of B2B commerce. Amazon Business is one of Amazon's fastest-growing initiatives focused on serving business customers, from individual professionals to large institutions, with unique and complex purchasing needs. Our customers require sophisticated solutions that go beyond traditional B2C experiences, including bulk purchasing, approval workflows, and business-grade service support. The AB-MSET Applied Science team focuses on building intelligent systems for delivering personalized, contextual service experiences throughout the customer lifecycle. We apply advanced machine learning techniques to develop sophisticated intent detection models for business customer service needs, create intelligent matching algorithms for optimal service routing based on multiple variables including customer value, maturity, effort, and issue complexity, build predictive models to enable proactive service interventions, design recommendation systems for self-service solutions, and develop ML models for automated service resolution. As an Applied Scientist on the team, you will design and develop state-of-the-art ML models for service intent classification, routing optimization, and customer experience personalization. You will analyze large-scale business customer interaction data to identify patterns and opportunities for automation, create scalable solutions for complex B2B service scenarios using advanced ML techniques, and work closely with engineering teams to implement and deploy models in production. You will collaborate with business stakeholders to identify opportunities for ML applications, establish automated processes for model development, validation, and maintenance, lead research initiatives to advance the state-of-the-art in B2B service science, and mentor other scientists and engineers in applying ML techniques to business problems.

The Marketing Measurement & Performance Support (MAPS) organization is looking for a Science Manager, interested in leading a team of Economists, Data Scientists and Applied Scientists in designing a measurement system to solve one of the most challenging business problems in marketing measurement. This exceptional leader will develop solutions combining experimental evidence, observational models and decision frameworks to redefine brand marketing measurement. The MAPS organization’s mission is to be the most trusted source of measurement science solutions to drive marketing investment decisions across Amazon. The MAPS team provides incrementality, efficiency measurement services and decision support to marketing stakeholders across Amazon’s Stores suit of businesses. MAPS applies industry leading causal inference models and designs experiments to measure omni-channel effectiveness of marketing campaigns from these businesses worldwide. Our outputs shape Amazon product and marketing teams’ decisions and therefore how Amazon customers see, use, and value their experience with Amazon. As a Science Manager, you will lead a team of scientists to develop state-of-the-art models, while collaborating with other scientists, businesses, marketers, and software teams to solve key challenges facing the teams. Such challenges include measuring the incremental impact of multi-channel marketing portfolios, estimating the impact on long term inter-related customer actions, and scaling measurement solutions for WW marketplaces. Unlike many companies who buy existing off-the-shelf marketing measurement systems, we are responsible for studying, designing, and building systems to serve Amazon’s suite of businesses. Our team members have an opportunity to be on the forefront of marketing measurement thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, economists and software developers in the business. Key job responsibilities In this role, you will be a people manager and a technical leader in Econometric research with significant scope, impact, and high visibility. You will own developing the next generation of Causal Marketing-Mix-Media (MMM) models combining experimental evidence with observational econometric techniques. Your solution will deliver to business leaders accurate and actionable incrementality estimates and recommendations to optimize their marketing portfolio. As a successful Science Manager, you can navigate ambiguity, lead problem solving, guide development of new frameworks, and credibly interface between technical teams and business stakeholders. You are an innovator who can push the limits on what’s scientifically possible with a razor sharp focus on measurable business impact. You will coach and guide scientists in your team across different job families including Economists, Data Scientists and Applied Scientists to grow the team’s talent and scale the impact of your work.

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.

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.