Careers

The People eXperience Technology (PXT) Central Science (PXTCS)'s mission is to make PXT the most scientific, technologically proficient, and inclusive HR organization in the world. PXTCS does this by accelerating scientific rigor in business-led initiatives, working backwards from employee experience, and delivering science-driven products that improve the well-being of and value of work for Amazonian worldwide. We are seeking an Applied Scientist to build production machine learning systems that turn Amazon's employee listening signals into insight at scale. You will design, develop, and deploy ML solutions that transform large volumes of employee feedback — survey responses, free-text comments, and other listening channels — into signals that drive strategic decision-making across the organization. In this role, you will work on challenging problems spanning natural language processing, large language models, predictive modeling, and generative AI applications applied to employee experience. You will collaborate with cross-functional teams to translate ambiguous business challenges into rigorous technical solutions. Key job responsibilities - Apply and adapt state-of-the-art NLP and machine learning techniques to extract structured, reliable signals from employee listening data (surveys, free-text feedback, and related channels), using reasonable assumptions, data, and customer requirements. - Design, develop, and implement small-to-medium ML components on top of the employee listening system — such as theme and sentiment extraction, text classification, and aggregated signal generation — with input and guidance from senior scientists, taking ownership of the code in your components. - Write secure, stable, testable, maintainable, well-reviewed code (at the SDE I bar) to deliver solutions into production that benefit customers and the business. - Design features and signals responsibly, applying privacy-preserving practices appropriate to sensitive employee data. - Collaborate with cross-functional partners to understand business context and impact, and help mentor interns. - Communicate complex technical concepts to diverse audiences, from technical peers to senior leadership. About the team The People eXperience and Technology Central Science Team (PXTCS) uses economics, applied science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We build and apply science on top of Amazon's employee listening systems — turning what Amazonians tell us into products and insights that improve their experience. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.

About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As an Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.

About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As an Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.

Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon International Technology Team is hiring Applied Scientists to work in our Software Development Center in Sao Paulo. The Intech team builds International extensions and new features of the Amazon.com web site for individual countries and creates systems to support Amazon operations. We have already worked in Germany, France, UK, India, China, Italy, Brazil and more. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning and big data, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise senior leadership, both tech and non-tech. - Make technical trade-offs between short-term needs and long-term goals.

Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. We are looking for an Applied Scientist to join our journey to build a Prime Video Sports tech team in Israel from the ground up. Our team focuses on developing products that personalize the customer experience and provide real-time insights using Computer Vision (CV) and Machine Learning (ML). You will get the chance to work on greenfield, large-scale science and engineering projects. Key job responsibilities We are looking for an Applied Scientist to lead the development of new Computer Vision algorithms and end-to-end solutions. You will research, design, develop, and deploy algorithms into production pipelines as part of a team of applied scientists and software development engineers. You will also drive publications of original work as patents and, optionally, in top-tier Computer Vision and Machine Learning conferences. This role involves collaborating across cross-functional teams and systems, and navigating evolving technical challenges in a greenfield environment. About the team In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. Today, Amazon holds exclusive broadcasting rights to major leagues including NFL Thursday Night Football, Tennis major Roland-Garros, and the English Premium League, broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content it offers, but the depth of the experience. This is a unique opportunity to be at the forefront of a program that will transform how customers enjoy live sports streaming.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. Key job responsibilities Lead analytical methods essential for product characterization; Make data driven decisions with long term vision; Work effectively with stakeholders to advance program development; Manage 3rd party vendors to deliver analytical milestones;

We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. In this role, you will be developing Agentic Large Language Models (LLMs) and/or Vision Large Language Models (VLM) to unlock new shopping experiences. Experience with Reinforcement Learning (RL) and LLM Post-training is preferred. You’ll be working with talented scientists, engineers, product managers, and technical program managers to innovate on behalf of our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!

The Foundational AI (FAI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. A day in the life You start your morning reviewing experimental results from a model training run you kicked off the previous afternoon, analyzing metrics and refining your approach. By mid-morning, you are whiteboarding a new algorithmic design with engineering partners, working through system integration details. After lunch, you lead a science review where you present findings and gather feedback from peers. Later, you pair with a junior scientist to debug a data pipeline issue, turning it into a coaching moment. Your day wraps up drafting a section of a research paper capturing your team's latest results. About the team The FAI team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

Love using AI to solve big, messy problems that actually move a business? Join Sales AI as an Applied Scientist and own the science behind how Amazon Ads helps every advertiser grow, from understanding where each brand is on its growth journey to building the GenAI agents account teams use every day. Advertising is one of the fastest growing parts of our business, and science sits at the heart of how we help brands succeed. Our scientists work on two connected problems. The first is figuring out, for each brand, the smartest next step it can take to grow, and turning that into a clear, personalized recommendation. The second is building LLM-powered assistants and agents that put that intelligence into the hands of the people who work with those brands, accurately and at scale. If you want real ownership, hard problems, and models that ship, we'd love to talk. Key job responsibilities You'll own production science systems that account teams rely on every day. For example, you might build the recommendation engine that maps where each advertiser is in their growth journey and what their best next step is, or improve the accuracy of the LLM-powered assistants and agents that account teams use to prepare for and run customer conversations. Day to day that means: - Turn ambiguous business problems into well-defined science problems, and choose the right approach to solve them - Build, ship, and continuously improve models in production, and own their quality once they launch - Measure whether our models really move the business, through A/B tests, causal analysis, and offline evaluation - Partner with engineers and product managers to take ideas from early research into production - Set the technical direction for your area, raise the scientific bar, and mentor other scientists - Explain complex methods and trade-offs clearly to engineers, product partners, and sales leaders A day in the life No two days look the same. You might start by testing a new modeling approach, then review evaluation results with engineers to decide whether a model is ready to launch. Later you could meet with product partners to agree on what to build next, or dig into data to understand why a recommendation isn't landing. Your customers are the account teams who work directly with brands, and the brands themselves, who rely on your work to decide where to invest and how to grow. About the team Sales AI is a central science and data engineering organization within Amazon Advertising Sales. Our mission is to power selling motions and account team workflows with state-of-the-art AI and ML services. We build advertiser insights, recommendations, and generative AI-powered applications woven throughout account team workflows. We care about a high scientific bar, real business impact, and taking ideas all the way into production. Most of all, we like working with curious people who want to drive real, lasting impact for our customers.

Pricing is one of the most consequential decisions Amazon makes, and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science team builds the machine learning (ML) systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value (LTV) frameworks, and the experimentation infrastructure that validates whether pricing changes actually work. We're looking for a Research Scientist to own causal inference at the intersection of ML and pricing experimentation. Our team has identified a real gap: the methodological bridge between econometric analysis and production-scale ML pipelines needs a practitioner who lives in both worlds. You'll build conditional average treatment effect (CATE) estimation models, design analysis workflows for pricing experiments, and develop reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. If you're a scientist who wants to tackle hard causal identification problems in a high-stakes production environment, and who finds satisfaction in making rigorous methods accessible to a broader team, this role is for you. Key job responsibilities - Design, train, evaluate, and deploy end-to-end causal ML pipelines for pricing use cases, including CATE estimation models and heterogeneous treatment effect frameworks. - Serve as the team's causal ML methodology expert — defining identification strategies, model selection criteria, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. - Contribute causal analysis methodology to pricing A/B test post-analysis and build reusable tooling that economists and other non-ML scientists can adopt without requiring deep ML expertise. - Assess emerging causal inference methods (e.g., synthetic difference-in-differences, generalized random forests, causal representation learning) for applicability to Amazon's pricing context, and author internal methodology proposals for adoption. - Partner with senior economists on identification strategy and causal assumptions, collaborate with software and data engineering partners on production deployment, and align with product managers on experiment design requirements. A day in the life Your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given week you might investigate an optimization anomaly in simulation and trace it to an unmodeled market dynamic, then design an offline evaluation framework to benchmark competing approaches before committing to online testing. You'll collaborate with senior economists on identification strategy for a pricing experiment, present a science proposal for incorporating a new competitiveness signal into an optimization system, or work with the experimentation platform team on randomization design. You'll also have opportunities to write up novel scientific findings for submission to venues such as KDD, NeurIPS, or the ACM Conference on Economics and Computation. About the team The P2 Optimization Science team sits at the center of Amazon's pricing decisions. We build the ML systems, causal inference pipelines, and experimentation infrastructure that determine how prices are set across Amazon's global catalog. Our team brings together research scientists, economists, and engineers who collaborate closely to turn rigorous methodology into production-quality systems with measurable business impact. We value scientific rigor, clear communication across disciplines, and a culture where every team member's work is designed to be extensible and useful beyond their own project. If you want your science to directly influence how hundreds of millions of customers experience Amazon, this is the team to join.