Amazon

Applied Scientist role focused on developing and deploying personalized recommendation systems using state-of-the-art modeling and GenAI techniques for Amazon's consumer shopping experience. The role involves building data-driven models, developing new signals, using supervised and uplift learning, contributing to production code and tooling, designing A/B tests, and publishing research.

This role focuses on developing and deploying personalized recommendation systems for Amazon customers, integrating GenAI techniques, and conducting research. The primary deliverable is a customer-facing AI product.

This role focuses on developing and deploying data-driven models for personalized shopping experiences using state-of-the-art recommendation systems and GenAI techniques. The scientist will design, develop, evaluate, and update models, create new signals, use supervised and uplift learning, design A/B tests, and work with large-scale distributed machine learning. The role also involves presenting and publishing research, mentoring junior scientists, and collaborating with business and engineering teams. Experience with causal machine learning and evaluating AI systems is required.

Applied Scientist III role focused on designing, developing, evaluating, deploying, and updating data-driven models and analytical solutions for machine learning and natural language applications. The role involves applying statistical modeling, optimization, and ML techniques, building and deploying models in production, and researching novel ML approaches. Requires a Master's degree (or equivalent experience) in a related field and experience in programming and developing supervised/unsupervised ML models.

Applied Scientist role focused on developing and launching AI solutions for Prime Video's personalization and discovery systems, involving deep learning, GenAI, RL, and optimization. The role requires end-to-end ownership, experimental design (offline and A/B testing), and publishing research findings. Experience with recommender systems and personalization is preferred.

Applied Scientist role focused on building and evaluating ML models for life sciences applications, specifically protein biology. Requires a PhD in a related field and expertise in ML/deep learning, with a preference for publication experience.

Applied Scientist role focused on developing and launching customer-obsessed pricing algorithms for billions of products worldwide. This involves leveraging large-scale multi-modal datasets, applying machine learning, predictive modeling, causal inference, and reinforcement learning to optimize pricing strategies and tools for sellers. The role emphasizes collaboration with product and engineering teams, continuous learning, and delivering business impact through innovative pricing solutions.

Applied Scientist role focused on Generative AI and quantitative modeling for Amazon Advertising Sales. The role involves conceptualizing and leading research on ML/GenAI solutions, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. Key areas include optimizing sales business, improving work efficiency through GenAI, and developing advertiser insights and recommendations.

Applied Scientist role focused on developing and deploying machine learning models to optimize marketing initiatives and predict customer behavior for Amazon's consumer business. Requires strong quantitative modeling, statistical methods, and experience with deep learning.

Research Scientist II Intern/Co-op role in Amazon's Personal Robotics Group, focusing on developing intelligent robotic products and systems. The role involves research in areas like robotics manipulation, autonomous navigation, locomotion, controls, perception, robot learning, AI, ML, computer vision, LLMs, and HRI, with a goal of impacting millions of customers. The position is for PhD students and involves working on scalable research projects.

Research scientist role focused on developing new AI technologies for personalization, specifically in recommendations, information retrieval, and fine-tuning LLMs, for Amazon's e-commerce platform. The role involves research, design, development, and launching new features and systems, with a strong emphasis on innovation and customer impact.

The role involves applying deep learning, neural networks, and transformer architectures to price prediction and forecasting problems within Amazon's Pricing Optimization group. It focuses on developing and deploying ML models at scale to improve pricing and promotion strategies, requiring collaboration with product, engineering, and science teams.

Internship role focused on machine learning, deep learning, generative AI, LLMs, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods. The intern will own the design and development of end-to-end systems, write technical white papers, create roadmaps, and drive production-level projects. Collaboration with scientists and other interns is expected, with opportunities to design new algorithms and models.

The Economist II, Featured Merchant Algorithm role at Amazon focuses on developing and deploying machine learning models that determine the 'Buy Box' offer and surrounding customer experience on Amazon's platforms. This involves ranking and selecting the best offer based on factors like price, availability, and delivery, impacting billions of transactions daily. The role requires a PhD in economics, strong data-driven decision-making, collaboration with cross-functional teams, and expertise in prediction and forecasting with large datasets.

Postdoctoral Scientist role in Amazon Robotics focusing on computer vision and manipulation challenges in warehouses, involving developing robotic manipulators with machine learning approaches. The role requires innovation in 3D scene understanding and item identification for robotic interaction.

This internship role focuses on applying data science and machine learning to solve complex problems in robotics at Amazon. Interns will design and implement solutions, research new algorithms, collaborate with experts, and build tools to improve robotics analytics and customer experience. The role involves working with various AI/ML fields including computer vision, LLMs, and deep learning within a robotics context.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods. Focus on designing and implementing state-of-the-art solutions for complex business problems, potentially leading to production-level projects and technical white papers. Collaboration with scientists and other interns is expected.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods. Focus on designing and developing end-to-end systems, writing technical white papers, creating roadmaps, and driving production-level projects. Opportunity to design new algorithms and models, potentially publish work at top-tier conferences.

MS or PhD student internship focused on research in machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, OR, quantum computing, automated reasoning, or formal methods. The role involves designing and developing end-to-end systems, writing technical papers, creating roadmaps, and driving production-level projects. Experience with publications at top-tier conferences and solving business problems with ML/data mining/statistical algorithms is preferred.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, OR, quantum computing, automated reasoning, or formal methods. Focus on inventing, designing, and implementing state-of-the-art solutions for complex problems. Will own design and development of end-to-end systems, write technical white papers, create roadmaps, and drive production-level projects. Opportunity to design new algorithms and models, and deploy them into production, potentially publishing work at top-tier conferences.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech, robotics, vision, optimization, OR, quantum computing, automated reasoning, or formal methods. Focus on designing and developing end-to-end systems, writing technical white papers, creating roadmaps, and driving production-level projects. Opportunity to design new algorithms and models, deploy solutions into production, and potentially publish work.

Research Scientist role focused on applying machine learning and data-driven solutions to optimize Amazon's last-mile delivery logistics. The role involves building ML models for business applications, developing scalable processes, and making strategic, data-driven decisions to improve customer experience and operational efficiency within the logistics network.

Applied Scientist II at Amazon Prime Video focusing on personalization and discovery science. The role involves developing ML models for recommendation and search systems using deep learning, online learning, and optimization methods. It requires staying updated with the latest modeling techniques, publishing research findings, and applying advanced approaches like foundation models to solve cold-start problems and discover niche customer interests. The scientist will work on highly scalable page personalization solutions and collaborate with engineers and product managers.

Economist to develop science behind measuring and optimizing B2B marketing investment for AWS. This is a greenfield opportunity to build a brand measurement capability from the ground up, defining frameworks, methodologies, and best practices. The role involves developing econometric models to estimate the causal impact of brand awareness on customer preferences, acquisition costs, and revenue, and quantifying optimal brand investment and returns. It requires applying causal inference methods suited to long-horizon, slow-moving brand effects and partnering with marketing, finance, and strategy teams to translate findings into actionable recommendations. The role also involves consulting with business stakeholders and staying current on brand measurement literature.

Research Scientist role focused on measuring and optimizing marketing activities for Amazon Music using causal models and RCTs. The role involves designing, building, and validating causal models, conducting randomized controlled trials, performing statistical analysis, and providing data-driven recommendations to marketing teams. It requires a strong background in consumer research, causal inference, and statistical analysis, with the ability to translate complex data into actionable insights.

Masters or PhD student internship in Data Science at Amazon, focusing on developing and implementing algorithms for scientific solutions, A/B testing, and estimating customer satisfaction. The role involves working closely with scientists and engineers on customer-obsessed research and creating scalable projects.

This role is for a Sr. Principal Scientist focused on Stores Economics and Science at Amazon. The scientist will work with ML scientists to validate models, translate analysis into business actions, and manage data analysis and modeling. The team uses economics, statistics, and ML to understand Amazon's economy and aims to drive AI adoption across the company.

Economist role focused on applying economic methods, statistics, and machine learning to improve business processes and employee well-being. Involves estimating models, designing experiments, and transforming prototypes into scalable solutions, working closely with engineers and computer scientists.

Research Scientist role focused on developing forecasting and optimization models using ML and GenAI to support real estate and store planning decisions for Amazon's grocery business. The role involves analyzing data, building end-to-end solutions, and presenting findings to leadership.

This role focuses on leveraging econometrics and ML models to optimize advertising strategies, influence business decisions, and perform hands-on analysis with large datasets. The Economist will work closely with software engineers to productionize models and run A/B experiments, impacting millions of customers.

Economist role focused on applying econometrics and ML to optimize advertising strategies, influence business decisions, and analyze experiments within Amazon's advertising business. Requires a PhD in economics and experience with large datasets and statistical software.

This role is for a Senior Research Scientist focused on materials chemistry innovation within Amazon's WW Sustainability team. The primary focus is on hands-on experimental research in materials synthesis, characterization, and performance testing to accelerate the discovery of sustainable materials. While the role involves developing scientific models and supporting the data foundation for AI models, its core is experimental materials science, not AI/ML model development or deployment.

Senior Economist role focused on building innovative solutions using econometrics, machine learning, and experimentation for Amazon Retail's product assortment. The role involves developing econometric models and experiments to measure customer and financial impact, influencing business decisions, and collaborating with a multidisciplinary team.

The JP Economics and Decision Sciences team is a central science team that applies rigorous economic theory, causal inference methods, and machine learning to solve complex business challenges across the JP marketplace and beyond. We work closely with JP business leaders to drive change at Amazon, focusing on solving long-term, ambiguous problems while providing advisory support for short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic and econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP-, EU-, and US-based interdisciplinary teams.

Research Scientist II role focused on psychometric aspects of exam development and operations, with a requirement to use ML, NLP, and GenAI for research and automation. The role involves developing and applying statistical and psychometric modeling to ensure exam validity and reliability, and automating psychometric workflows using R or Python. Experience with large-scale assessment programs and complex test designs is required.

Seeking a Sr. Quantitative UX Researcher to shape AI-native business applications, define research strategy, and influence human-AI interaction. This role involves owning experimental design, quantitative analysis, insight generation, and measurement strategies for AI-powered solutions. The researcher will work in 0 to 1 environments, define success metrics for agentic AI products, identify key behavioral signals, and invent AI-enabled tools and processes to scale research impact.

Senior Economist role focused on causal inference, machine learning, and empirical industrial organization to optimize resource allocation and pricing decisions within Amazon Devices. The role involves leading vision setting, methods innovation, and influencing business executives, with outputs integrated into scaled systems for automation and objective maximization.

This role focuses on applying economic expertise, causal inference, and machine learning to research and model factors driving workplace safety outcomes within Amazon's operations. The goal is to identify effective improvement programs and develop data-driven solutions to reduce risks and injuries, partnering with business and technical teams to deliver impactful insights and scalable data resources.

Principal Economist role focused on developing economic and science products for the Fee domain, specializing in supply chain systems and fees. The role involves developing causal links, pricing strategies, and econometric models for a two-sided marketplace, partnering with various science and technology teams. Requires experience with big data and machine learning or data science.

This role focuses on applying economics, behavioral science, statistics, and machine learning to identify process improvements within Amazon's People Experience and Technology (PXT) organization. The Economist will work with business partners and engineers to analyze data, estimate models, design pilots, measure impact, and implement solutions at scale, aiming to improve employee outcomes, hiring, development, and retention.

Internship role focused on research and development in quantum computing, involving theoretical and experimental physics, materials science, and hardware/software engineering. The role aims to develop novel, scalable solutions to advance the state-of-the-art in quantum computing, potentially leading to products for Amazon customers. Requires PhD enrollment and expertise in areas like superconducting qubits, quantum optics, and quantum error correction.

Principal Applied Scientist role focused on automated reasoning, privacy, and sovereignty within AWS. Requires a PhD, experience in professional software development, and programming in specific languages like O'Caml or Dafny. The role involves solving complex problems, leading design and implementation of solutions, providing cross-organizational technical influence, and mentoring others.

This role focuses on automated reasoning, privacy, and sovereignty within AWS, requiring deep knowledge in areas like SAT, SMT, symbolic simulation, and program analysis. The candidate will solve complex problems, lead design and implementation, and provide cross-organizational technical influence. While the company is in AI, the core craft of this role is not AI/ML model building but rather formal methods and reasoning.

This role focuses on automated reasoning, which is a subfield of AI, but the core responsibilities involve traditional computer science research in areas like SAT, SMT, theorem proving, and program analysis, rather than direct application or development of modern ML/LLM models. The role is classified as Research, with a primary stage of L0 (data/foundational research) and an AI score of 0 because it does not directly involve shipping ML models or agents.

This role focuses on automated reasoning, which is a subfield of AI, but the core responsibilities and required skills (SAT, SMT, theorem proving, program analysis) are more aligned with traditional computer science research and formal methods rather than the development or deployment of machine learning models or agents. The role is classified as Research within the Enterprise AI domain, with an AI maturity signal of Exploring, and a primary stage of L0 (Data) due to the foundational nature of the research in logic and formal systems, which can be seen as a form of data preparation or foundational work for more complex AI systems. However, since the role does not directly ship ML models or agents, it is marked as not AI-related.

This role focuses on automated reasoning, privacy, and sovereignty within AWS, solving complex problems with deep scientific innovation and providing cross-organizational technical influence. It involves developing strategic plans for new solutions and mentoring others.

This role focuses on automated reasoning, which is a subfield of AI, but the core responsibilities and required skills (SAT, SMT, theorem proving, program analysis) do not directly involve building or shipping AI/ML models or agents as defined by the schema's stages L0-L6. The role is more aligned with traditional computer science research in formal methods and logic, rather than the direct application or development of AI/ML systems.

The role involves driving the science and engineering behind an open-source authorization language (Cedar) and its integration into cloud-native platforms like Kubernetes. Responsibilities include advancing formal verification and analysis capabilities, developing tools to identify security/safety issues in authorization policies, creating and productizing prototypes, and contributing to the open-source codebase as a maintainer. The role also involves representing the project at technical conferences, advocating for adoption, and collaborating with the open-source community to advance project maturity.