Careers

Some of the hardest problems in security are waiting on models that do not exist yet. As a Principal Applied Scientist, you will create them. You start from the strongest models in the world and push them to excel at problems no off-the-shelf model has ever seen. This is one of the largest AI infrastructure programs AWS has ever taken on, and you will train on latest-generation GPU hardware, among the first anywhere to put it to work at this scale. Much of this has no established playbook. There is no benchmark to tell you a model is ready, so you will invent both the method that gets it there and the measure that proves it. The lab is purpose-built for this work and you join at the start, helping shape its technical direction and building the methods and evaluations that carry forward into everything it does next. This is a hands-on role. You will research the state of the art science, make the calls on how models are trained and evaluated, write code and work alongside the engineers who run the infrastructure, and partner directly with the analysts and operators who depend on the work. You will also explain what the models can and cannot yet do, in plain terms, to the people whose decisions rely on them. The candidate selected must obtain and maintain a security clearance at the TS/SCI with polygraph level. Upon start, the selected candidate will be sponsored for a commensurate clearance for each government agency for which they perform AWS work. Key job responsibilities • Own the end-to-end adaptation of frontier models for complex reasoning tasks, from supervised fine-tuning and post-training through reinforcement learning, delivered as production systems people can rely on in the field • Define what "good" means for each task, building the evaluation sets, test harnesses, and metrics that measure model quality where no public benchmark exists • Build and own the data pipelines that turn raw operational data into high-signal training and evaluation sets, including curation, quality scoring, provenance tracking, and strict leakage and contamination controls • Judge when a problem needs a genuinely new method versus an extension of an existing one, and make the call with evidence • Choose the training and adaptation methods, including parameter-efficient techniques, that turn serious dedicated compute into the largest gains in capability • Extend adapted models into new priority domains, owning transferability, generalization, and calibration under real-world distribution shift • Run data-preparation and human-in-the-loop feedback loops with the analysts who know the problems best, turning their expertise into better data and sharper evaluations • Package methods and evaluations so they can be reused as the lab's work grows, and shared with Amazon's model teams • Represent the lab's science in technical reviews and to program and technical leaders, surfacing what the models can do and where the risks are • Help set the technical bar for the lab, and mentor scientists as the team grows A day in the life You start the morning reviewing an overnight fine-tuning run against an evaluation suite you built, checking whether the adapted model beats the base on the metrics that matter most. Mid-morning, you work with the engineers who run the infrastructure to get a large training run moving and squeeze the most capability out of it. After lunch, you sit with domain analysts to turn their feedback into a cleaner, better-labeled evaluation set for a change-detection task, then decide whether the next gain calls for a new method or an extension of one you already have. You close the day documenting that method and evaluation so the rest of the lab can build on it, and writing a short, plain-language readout of what the model can and cannot yet do for the people who depend on it. About the team The Mission AI Model Lab is the science team behind AWS's Mission AI program. We take existing models and make them useful for problems that matter to national security, working on dedicated AI infrastructure built for exactly that. This lab is purpose-built and these are among its first scientists, so the people who join now help shape how it works and what it builds. We sit where frontier-model research meets national security delivery, and we value invention on genuinely new problems, evaluation we can defend, and plain talk with the people who rely on what we build.

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art 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. Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. About this role In this role, you'll employ scalable machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) techniques to detect and predict fraudulent activities, enhance fraud investigation capabilities, and develop advanced fraud protection and defense mechanisms. You'll leverage these technologies to analyze complex patterns in transaction data, identify anomalies, and create predictive models that can anticipate potential fraud before it occurs. Your work will be crucial in safeguarding the company's assets, protecting customers from financial harm, and maintaining the integrity of our systems. You'll translate intricate fraud patterns into actionable insights, enabling rapid response to emerging threats and informing critical business decisions related to risk management. You'll operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects. As an Applied Scientist, you will... - Protect Audible’s customers and content creators against the onslaught of AI-generated fraud - Develop Amazon-scale data engineering & modeling pipelines. - Imagine and invent before the business asks, and create novel fraud detection and mitigation solutions using innovative approaches. - Work closely with other data scientists, ML experts, engineers as well as business across the globe, and on cross-disciplinary efforts with other scientists within Amazon. - Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others. ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

Are you passionate about leveraging data and economics to enhance customer experience across Amazon's diverse businesses? The Customer Experience and Business Trends (CXBT) organization is seeking an Economist to join our Benchmarking Economics Analytics and Measurement (BEAM) team. Our mission is to drive customer experience improvements through innovative economic modeling, advanced analytics, and scalable scientific solutions. As an Economist on our team, you will collaborate with senior management, business stakeholders, scientists, engineers, and economics leadership to solve complex business challenges across Amazon's business lines. You'll develop sophisticated econometric models using our world-class data systems, applying diverse methodologies spanning causal inference, machine learning, and generative AI. In this fast-paced environment, you'll tackle challenging problems that directly influence strategic decision-making and drive measurable business impact. Key job responsibilities 1. Develop economic theory and deliver causal machine learning models at scale 2. Collaborate with cross-functional teams to translate research into scalable solutions 3. Write effective business narratives and scientific papers to communicate to both business and technical audiences 4. Drive data-driven decision making to improve customer experience About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Data Scientists, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.

Are you a MS or PhD student interested in a 2026 Applied Science Internship in the field of Robotics? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way? If this describes you, come join our research teams at Amazon. Key job responsibilities As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. We are looking for scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Are you a MS or PhD student interested in a 2026 Applied Science Internship in the field of Robotics? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way? If this describes you, come join our research teams at Amazon. Key job responsibilities As an Applied Science Intern, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. We are looking for scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

AWS is seeking a Data Scientist to join our Consumer Experience team. We're looking for someone who is curious about large-scale data challenges and motivated to apply ML/AI and advanced analytics to solve real customer problems. Reporting to the Science Lead, this role supports the analytical capabilities of AWS by applying emerging technologies, contributing to data science best practices, and partnering with stakeholders to deepen understanding of the AWS customer experience. The ideal candidate has experience working in complex data and analytics environments, with hands-on skills in Machine Learning and advanced analytics techniques — preferably within cloud computing. You should be comfortable building models and equally comfortable explaining what they mean to a non-technical audience. Key job responsibilities - Apply statistical analysis, ML, and complex data mining techniques to deliver insights that shape the AWS customer experience. - Build production-ready analytics solutions that support effective business decisions. - Partner with product, engineering, and design teams to develop and launch data products. - Contribute to the adoption of Gen AI and ML-based approaches across the data science practice, including building proof-of-concept AI-driven customer use cases. - Communicate technical findings clearly to both technical and non-technical stakeholders. - Bring new ideas — share approaches, tools, and methods that raise the bar for the team.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Santa Monica, California Job Number: AMZ10564053 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. 40 hours / week, 8:00am-5:00pm, Salary Range: $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that improve Amazon's customers experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Customer Fulfillment & Prime analytics team. We're seeking an exceptional scientist who can revolutionize how we delight our customers. As a key member of our team, you'll develop and deploy machine learning systems that analyze millions of customer interactions daily, ensuring state of art experience for customers across 10 countries. Your work will directly impact the shopping experience for hundreds of millions of customers worldwide. Key job responsibilities • Ability to work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models. • Strong foundation in machine learning and statistical modeling. • Experience deploying ML solutions into a production environment. • Apply machine learning and statistical techniques to create scalable abuse-detection solutions that identify fraudulent customer behavior, rings of accounts, identity change, holistic customer risk, and marketplace manipulation schemes. • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions. • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that scale to millions of customers across 10+ countries. • Willing to travel to Mexico (MX) and Brazil (BR). • Comfortable working CET hours to maintain overlap with Mexico and Brazil business teams. About the team You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that improve Amazon's customers experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Customer Fulfillment & Prime analytics team. We're seeking an exceptional scientist who can revolutionize how we delight our customers. As a key member of our team, you'll develop and deploy machine learning systems that analyze millions of customer interactions daily, ensuring state of art experience for customers across 10 countries. Your work will directly impact the shopping experience for hundreds of millions of customers worldwide. Key job responsibilities • Ability to work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models. • Strong foundation in machine learning and statistical modeling. • Experience deploying ML solutions into a production environment. • Apply machine learning and statistical techniques to create scalable abuse-detection solutions that identify fraudulent customer behavior, rings of accounts, identity change, holistic customer risk, and marketplace manipulation schemes. • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions. • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that scale to millions of customers across 10+ countries. • Willing to travel to Mexico (MX) and Brazil (BR). • Comfortable working CET hours to maintain overlap with Mexico and Brazil business teams. About the team You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.