Careers

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 seeking a stellar Machine Learning scientist who has experience developing and shipping large scale ML products with visible customer impact. We would prefer if your previous work has been in developing scalable Agentic, RL or forecasting systems. Strong academic background in Statistics, Machine Learning & Deep Learning is required with Tier -1 publications being a plus. • Master’s degree in CS or ML related fields • Scientist/Tech Lead creating and shipping impactful ML products. • Ability to write clear, structured and modularized code in Python. • Expertise in Deep Learning frameworks such as Tensorflow, Keras and Pytorch & Agentic frameworks such as LangChain, Crew AI etc. • Industry experience designing complex scalable AI systems. • Experience and technical expertise across various science domains. Crucial ones being statistics, deep & machine learning. • Experience creating data pipelines & proficient in querying data from Spark/HIVE/Redshift/other large scale data warehousing platforms. • Expert in distilling informal customer requirements into problem definitions, dealing with ambiguity and formulating ML products to solve these problems. Key job responsibilities In this position, you will be a key contributor (with direct leadership visibility) building, productionizing (real & batch) and measuring impact of state of the art personalized Gen AI systems for Amazon global selling partners and contribute to Amazon wide research in this area in the form of publications and white papers. You will work with global leaders and teams across time zones on a regular basis. About the team Millions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem our team plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon. We help independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. We are pushing the boundaries of these machine learning tools in areas of Agentic, recommendation and forecasting systems to help our sellers sell more and across borders.

Job summary We are seeking a stellar Machine Learning scientist who has 1+ years of industry experience developing and shipping large scale ML products with visible customer impact. We would prefer if your previous work has been in developing scalable Agentic, RL or forecasting systems. Strong academic background in Statistics, Machine Learning & Deep Learning is required with Tier -1 publications being a plus. Minimum requirements • Master’s degree in CS or ML related field and 1+ yrs experience working as a Machine Learning Scientist/Tech Lead creating and shipping impactful ML products. • Ability to write clear, structured and modularized code in Python. • Expertise in Deep Learning frameworks such as Tensorflow, Keras and Pytorch & Agentic frameworks such as LangChain, Crew AI etc. • Industry experience designing complex scalable AI systems. • Experience and technical expertise across various science domains. Crucial ones being statistics, deep & machine learning. • Experience creating data pipelines & proficient in querying data from Spark/HIVE/Redshift/other large scale data warehousing platforms. • Expert in distilling informal customer requirements into problem definitions, dealing with ambiguity and formulating ML products to solve these problems. Preferred Qualification • PhD in CS or a highly computation field with 1-2+ years of industry experience shipping impactful ML products. • Industry experience designing and creating state of the art recommendation and ranking systems using neural & causal techniques. • Academic publications in the areas of Agentic systems, reinforcement learning etc. • Industry experience working and delivering in an Agile/Scrum environment. Key job responsibilities In this position, you will be a key contributor (with direct leadership visibility) building, productionizing (real & batch) and measuring impact of state of the art personalized Gen AI systems for Amazon global selling partners and contribute to Amazon wide research in this area in the form of publications and white papers. You will work with global leaders and teams across time zones on a regular basis. About the team Millions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem our team plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon. In Jeff’s own words (2019 Q1 shareholders letter): “Something strange and remarkable has happened over the last 20 years. Take a look at these numbers: 1999 - 3% …… 2018 - 58%. The % represent the share of physical gross merchandise sales sold on Amazon by independent third-party sellers – mostly small- and medium-sized businesses – as opposed to Amazon retail's own first party sales. Third-party sales have grown from 3% of the total to 58%. To put it bluntly: Third-party sellers are kicking our first party butt. Badly.…. Why were independent sellers able to grow so much faster than Amazon's own highly organized first-party sales organization? There isn't one answer, but we do know one extremely important part of the answer: We helped independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. We are pushing the boundaries of these machine learning tools in areas of Agentic, recommendation and forecasting systems to help our sellers sell more and across borders.

Job summary We are seeking a stellar Machine Learning scientist who has 3+ years of industry experience developing and shipping large scale ML products with visible customer impact. We would prefer if your previous work has been in developing scalable Agentic, RL or forecasting systems. Strong academic background in Statistics, Machine Learning & Deep Learning is required with Tier -1 publications being a plus. Minimum requirements • Master’s degree in CS or ML related field and 3+ yrs experience working as a Machine Learning Scientist/Tech Lead creating and shipping impactful ML products. • Ability to write clear, structured and modularized code in Python. • Expertise in Deep Learning frameworks such as Tensorflow, Keras and Pytorch & Agentic frameworks such as LangChain, Crew AI etc. • Industry experience designing complex scalable AI systems. • Experience and technical expertise across various science domains. Crucial ones being statistics, deep & machine learning. • Experience creating data pipelines & proficient in querying data from Spark/HIVE/Redshift/other large scale data warehousing platforms. • Expert in distilling informal customer requirements into problem definitions, dealing with ambiguity and formulating ML products to solve these problems. Preferred Qualification • PhD in CS or a highly computation field with 2-3+ years of industry experience shipping impactful ML products. • Industry experience designing and creating state of the art recommendation and ranking systems using neural & causal techniques. • Academic publications in the areas of Agentic systems, reinforcement learning etc. • Industry experience working and delivering in an Agile/Scrum environment. Key job responsibilities In this position, you will be a key contributor (with direct leadership visibility) building, productionizing (real & batch) and measuring impact of state of the art personalized Gen AI systems for Amazon global selling partners and contribute to Amazon wide research in this area in the form of publications and white papers. You will work with global leaders and teams across time zones on a regular basis. About the team Millions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem our team plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon. In Jeff’s own words (2019 Q1 shareholders letter): “Something strange and remarkable has happened over the last 20 years. Take a look at these numbers: 1999 - 3% …… 2018 - 58%. The % represent the share of physical gross merchandise sales sold on Amazon by independent third-party sellers – mostly small- and medium-sized businesses – as opposed to Amazon retail's own first party sales. Third-party sales have grown from 3% of the total to 58%. To put it bluntly: Third-party sellers are kicking our first party butt. Badly.…. Why were independent sellers able to grow so much faster than Amazon's own highly organized first-party sales organization? There isn't one answer, but we do know one extremely important part of the answer: We helped independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. We are pushing the boundaries of these machine learning tools in areas of Agentic, recommendation and forecasting systems to help our sellers sell more and across borders.

We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference skillsets to solve real world problems. The intern will work in the area of advertising economics and develop models to measure the incremental effect from adopting full-funnel advertising strategy for Amazon sellers. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. Key job responsibilities As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include: - Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges - Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation - Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends - Build and refine comprehensive datasets for in-depth economic analysis - Present complex analytical findings to business leaders and stakeholders About the team The mission of the GMAC Econ (Global Media, Advertising and Corp Dev) Stores-Ads Science team is to understand how advertising interacts with Amazon Stores and identify opportunities where Stores can enhance the use of advertising signals in decision-making and vice versa to drive long-term economic value to shoppers, sellers/vendors, and Amazon.

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.

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.

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 excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: Analyze large-scale datasets using structural econometric techniques to solve complex business challenges Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models Building datasets and performing data analysis at scale Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value Build and refine comprehensive datasets for in-depth structural economic analysis Present complex analytical findings to business leaders and stakeholders

Passionate about books? The Amazon Books team is looking for a talented Applied Scientist II to help invent, design, and deliver science solutions to make it easier for millions of customers to find the next book they will love. In this role, you will - Be a part of a growing team of scientists, economists, engineers, analysts, and business partners. - Use Amazon’s large-scale computing and data resources to generate deep understandings of our customers and products. - Build highly accurate models (and/or agentic systems) to enhance the book reading & discovery experiences. - Design, implement, and deliver novel solutions to some of Amazon’s oldest problems. Key job responsibilities - Inspect science initiatives across Amazon to identify opportunities for application and scaling within book reading and discovery experiences. - Participate in team design, scoping, and prioritization discussions while mapping business goals to scientific problems and aligning business metrics with technical metrics. - Spearhead the design and implementation of new features through thorough research and collaboration with cross-functional teams. - Initiate the design, development, execution, and implementation of project components with input and guidance from team members. - Work with Software Development Engineers (SDEs) to deliver production-ready solutions that benefit customers and business operations. - Invent, refine, and develop solutions to ensure they meet customer needs and team objectives. - Demonstrate ability to use reasonable assumptions, data analysis, and customer requirements to solve complex problems. - Write secure, stable, testable, and maintainable code with minimal defects while taking full responsibility for your components. - Possess strong understanding of data structures, algorithms, model evaluation techniques, performance optimization, and trade-off analysis. - Follow engineering and scientific method best practices, including design reviews, model validation, and comprehensive testing. - Maintain current knowledge of research trends in your field and apply rigorous scrutiny to results and methodologies. A day in the life In this role, you will address complex Books customer challenges by developing innovative solutions that leverage the advancements in science. Working alongside a talented team of scientists, you will conduct research and execute experiments designed to enhance the Books reading and shopping experience. Your responsibilities will encompass close collaboration with cross-functional partner teams, including engineering, product management, and fellow scientists, to ensure optimal data quality, robust model development, and successful productionization of scientific solutions. Additionally, you will provide mentorship to other scientists, conduct reviews of their work, and contribute to the development of team roadmaps. About the team The team consists of a collaborative group of scientists, product leaders, and dedicated engineering teams. We work with multiple partner teams to leverage our systems to drive a diverse array of customer experiences, owned both by ourselves and others, that enable shoppers to easily find their perfect next read and enable delightful reading experiences that would make Kindle the best place to read.