Careers

Are you passionate about solving complex business problems at scale through Generative AI? Do you want to help build intelligent systems that reason, act, and learn from minimal supervision? If so, we have an exciting opportunity for you on Amazon's Trustworthy Shopping Experience (TSE) team. At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy. We do this in close partnership with our selling partners, empowering them with best-in-class tools and expertise to offer a high-quality, compliant selection that customers trust. As an Applied Scientist I, you will bring subject matter expertise in at least one relevant discipline (e.g., NLP, computer vision, representation learning, agentic architecture) to contribute to next-generation agentic AI solutions that automate complex manual investigation processes at Amazon scale. Working alongside senior scientists, you will map business goals—such as reducing cost-of-serving while maintaining trust and safety standards—to well-defined scientific problems and metrics. You will invent, refine, and experiment with solutions spanning agentic reasoning, self-supervised representation learning, few-shot adaptation, multimodal understanding, and model compression. With guidance from senior scientists, you will stay current on research trends and benchmark your results against the state of the art. You will help design and execute experiments to identify optimal solutions, initiating the development and implementation of small components with team guidance. You will write secure, stable, testable, and well-documented production code at the level of an SDE I, rigorously evaluating models and quantifying performance. You will handle data in accordance with Amazon policies, troubleshoot issues to root cause, and ensure your work does not put the company at risk. Your scope of influence will typically be at the self-level, with the possibility of mentoring interns. You will participate in team design and prioritization discussions, learn the business context behind TSE's products, and escalate problems with proposed solutions. You will publish internal technical reports and may contribute to peer-reviewed publications and external review activities when aligned with business needs. This role offers a unique opportunity to contribute to end-to-end AI development—from research through production—with your contributions serving hundreds of millions of customers within months, not years. Key job responsibilities •Contribute to the design and development of agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, including feedback and memory mechanisms, leveraging reinforcement learning techniques for agent decision-making and policy optimization, with input and guidance from senior scientists • Design and build expertise agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence with capabilities to handle feedback with long term as well as short term memory mechanisms. • Productionize large scale models built on top of SFT (Supervised Finetuning) and RFT (Reinforced fine tuning) approaches (GRPO with RLVR, Process/Outcome Reward Models), few shot approaches (Contrastive, Prototypical) based on multimodal datasets • Enhance on existing Automatic prompt optimization techniques (GEPA & beyond) towards agentic optimization given the ground truth datasets to improve agentic planning. • Build novel production ready Finetuned transformer architectures (using LORA/Q-LORA/LLM-JEPA etc) and conventional supervised & unsupervised ML solutions to aid the multiple potential automation requirements • Identify customer and business problems at project level; invent or extend state-of-the-art approaches for complex LLM workflows involving unstructured text, documents, images, and relational data •Contribute to building production-ready deep learning and conventional ML solutions, including multimodal fusion and cross-modal alignment techniques that seamlessly connect visual, textual, and relational understanding, to support automation requirements within your team's scope •May co-author research papers for peer-reviewed internal and/or external venues, including contributions in areas such as multimodal representation learning and vision-language modeling, and contribute to the wider scientific community by reviewing research submissions, when aligned with business needs •Prototype rapidly, iterate based on feedback, and deliver small components at SDE I level—including multimodal data pipelines and inference modules—that integrate into production-scale systems •Write secure, stable, testable, maintainable, and well-documented code, balancing model capability, deployment cost, and resource usage across multimodal architectures while understanding state-of-the-art data structures, algorithms, and performance tradeoffs •Rigorously test code and evaluate models across individual and combined modalities, quantifying their performance; troubleshoot issues, research root causes, and thoroughly resolve defects, leaving systems more maintainable •Participate in team design, scoping, and prioritization discussions through clear verbal and written communication; seek to learn the business context, science, and engineering behind your team's products, including how multimodal signals contribute to trust and safety decisions •Participate in engineering best practices with peer reviews; clearly document approaches and communicate design decisions; publish internal technical reports to institutionalize scientific learning •Help train and mentor scientist interns; identify and escalate problems with proposed solutions, taking ownership or ensuring clear hand-off to the right owner About the team Trustworthy Shopping Experience Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.

We build AI-powered tooling that enables security operations to scale with AWS's growth. Our portfolio includes generative AI incident response assistants, natural language-driven response, detection enrichment pipelines, and security data analytics platforms. Security analysts depend on these systems around the clock. We are hiring a Senior Applied Scientist to own the science strategy for our AI security response platform. You will define and execute the machine learning and AI roadmap across our service portfolio, from large language model-powered incident triage to anomaly detection in security telemetry. You will extend and invent techniques at the product level, partnering with software and security engineers to bring models from research into production systems that operate 24/7/365. You will be the scientific authority on the team, expected to teach, mentor, and set the technical bar for how we apply AI to security operations problems. This role requires deep expertise in natural language processing, generative AI, or a closely related discipline, combined with a demonstrated ability to translate scientific methods into production systems that solve real business problems. You will operate in high-ambiguity, high-consequence domains where your scientific judgment directly affects security outcomes for AWS. Key job responsibilities - Define and own the science strategy for the team's AI-powered security automation portfolio, including model selection, evaluation methodology, and research direction. - Design and implement LLM-powered systems for security incident triage, including retrieval-augmented generation, prompt engineering, and fine-tuning approaches that improve recommendation accuracy and reduce analyst toil. - Build anomaly detection and classification models across security telemetry data sources to surface threats, reduce false positives, and prioritize analyst attention. - Partner with software engineers to move models from experimentation to production. Define system-level technical requirements, guide adaptation to meet production constraints, and own model performance in deployment. - Develop evaluation frameworks and metrics that measure model effectiveness against security outcomes, not just standard ML benchmarks. - Mentor software and security engineers on ML best practices and raise the science bar across the team through design reviews, code reviews, and knowledge sharing. A day in the life You start by reviewing model performance dashboards for overnight incident triage recommendations, investigating a drift in precision for a specific detection category. Mid-morning, you lead a design review for a retrieval-augmented generation pipeline that will surface relevant runbooks during security incidents. After lunch, you pair with a security analyst to label edge cases that your current model misclassifies, turning operational feedback into training signal. You close the day writing an experiment plan to evaluate a new embedding approach for security log similarity, then sync with your manager on the quarterly science roadmap. About the team This team operates within AWS Security in a 24/7/365 organization that protects AWS's global cloud infrastructure. The team builds AI-powered security automation, data analytics platforms, and incident response tooling that security analysts depend on around the clock. We work at the intersection of machine learning, generative AI, and security operations. Our mission: give every security analyst the intelligent tooling they need to stay ahead of threats at AWS scale. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

We are seeking a stellar Data scientist who has experience developing science products, drive conversations with business stakeholders and visible customer impact. We would prefer if your previous work has been in scalable Agentic, RL or forecasting products. Strong academic background in Statistics, Machine Learning & Science is required with white paper publications/science cast studies about your work being a plus. • Master’s degree in statistics, CS or ML related fields • Scientist/Tech Lead creating and shipping impactful ML products. • Ability to write clear, structured and modularized scripts in Python. • Expertise in ML & Deep Learning frameworks such as Tensorflow, Keras and Pytorch & Agentic frameworks such as LangChain, Crew AI etc. • Industry experience working with complex 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.

We are looking for researchers who aim to build super-intelligent AI systems that leverage proof assistants to guide learning and reasoning. Our neuro-symbolic AI technology is applied across a wide range of science and engineering domains within Amazon, and you will join the team at the forefront of this research. As an Applied Scientist here, you will play a pivotal role in shaping the definition, vision, and development of product features from beginning to end. You will: * Define and implement new neuro-symbolic applications that employ scalable and efficient approaches to solve complex problems. * Work in an agile, startup-like development environment, where you are always working on the most important stuff. * Deliver high-quality scientific artifacts. About the team We work closely with academia. Our team includes an Amazon Scholar in mathematics, and we maintain active research collaborations with faculty at leading CS departments (MIT, Berkeley, CMU). Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: San Diego, California Job Number: AMZ9803634 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range $159,200/year to $215,300/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

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 (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on 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 * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation

Interested in influencing what customers around the world see when they turn on Prime Video? The Prime Video Personalization and Discovery team matches customers with the right content at the right time, at all touch points throughout the content discovery journey. We are looking for a customer-focused, solutions-oriented Principal Data Scientist to develop next-gen measurement and experimentation systems within Prime Video Personalization and Discovery. You'll be part of an embedded science team driving projects across product and engineering teams that ultimately influence what millions of customers around the world see when the log into Prime Video. The ideal candidate brings experience building experiment-based measurement systems at scale, excellent stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will build cross-functional support within Prime Video for high-quality, rigorous measurement, assess business problems, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. Key job responsibilities - Define and drive the multi-year vision for experiment-based measurement systems within Prime Video - Partner with product stakeholders and science peers to identify strategic data-driven opportunities to improve the customer experience - Communicate findings, conclusions, and recommendations to technical and non-technical business leaders across Prime Video - Educate senior leaders about and advocate for high-quality measurement as an input to data-driven decisions - Mentor junior scientists and review technical artifacts to ensure quality - Stay up-to-date on the latest data science tools, techniques, and best practices and help evangelize them across the organization

Do you want to help shape the future of Amazon's physical retail presence? Worldwide Grocery Stores (WWGS), Location Strategy and Analytics team is looking for an Research Scientist to join us in developing advanced forecasting models, optimization models, and analytical tools to support critical real estate and store planning decisions for Amazon's Worldwide Grocery business, including Whole Foods Market. Our team is responsible for developing predictive models and tools to support Real Estate and Topology analysts in making important decisions regarding our stores—including new store openings, relocations, closures, remodels, design, new formats, and more. We leverage statistical modeling, machine learning, and GenAI to build solutions for store sales forecasting, sales transfer effects, macrospace optimization, store network optimization, store network diffusion planning, and causal effects. As a Research Scientist on our team, you will apply your technical and analytical skills to tackle complex business problems and develop innovative solutions to improve our forecasting and decision-making capabilities. You will collaborate with a diverse team of scientists, economists, and business partners to identify opportunities, develop hypotheses, build internal products, and translate analytical insights into actionable recommendations for Executive Leadership. Key job responsibilities - Design and implement forecasting models and machine learning solutions to predict store performance and optimize our retail network. - Analyze large datasets to uncover insights and patterns related to store performance, customer behavior, and market dynamics. - Develop end-to-end solutions, tools and frameworks to scale our ML model development and data analysis. - Leverage GenAI models to enhance user interaction with our solutions, improve overall user experience, and build new features. - Present research findings and recommendations to scientists, business leaders, and executives. - Collaborate with cross-functional teams to drive adoption of models and insights. - Stay current on latest developments in relevant fields and propose innovative approaches. About the team We are a team of scientists passionate about leveraging data and advanced analytics to drive strategic decisions for Amazon's grocery business. Our work directly impacts Amazon's worldwide grocery store growth and development strategy. We foster a collaborative environment where team members are encouraged to think creatively, challenge assumptions, and pursue novel approaches to solving complex problems. Our team is at the forefront of applying a multitude of techniques - including GenAI - to improve our scientific solutions and products.

Come join the Device connectivity team in building the next generation of innovative wireless solution that create a magical experience on our products and services. We actively engage in strategic initiatives, foster partnerships with industry and academia, leverage foundational artificial intelligence and large language models to stay at the forefront of the technological advancements. Key job responsibilities As a Sr. Applied Scientist in the team, you will: - Seek to understand in depth the Devices and Services offering at Amazon, primarily focused on wireless solutions, and identify areas of opportunities to grow our business using AI solutions. - Design and lead AI roadmaps and solutions aimed at helping our customers have a delightful experience through our devices and service. - Work with our engineering partners and draw upon your experience to meet performance requirements within system constraints. - Identify untapped, high-risk technical and scientific directions, and devise new research directions that you will drive to completion and deliver. - Be responsible for communicating our AI innovations to the broader internal & external scientific community - Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. About the team Device Connectivity team is empowering possibilities through wireless innovation on our devices and through services, our vision is to design and develop transformative products and services that consistently exceed our customers' expectations.

Amazon Selling Partner Services (SPS) team's mission is to make Amazon the safest and most trusted place worldwide to transact online. Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. SPS safeguards every financial transaction across all Amazon sites. As such, SPS designs and builds the software systems, risk models and operational processes that minimize risk and maximize trust in Amazon.com. SPS organization is looking for a Data Scientist for its Forecasting and Planning Research team. The team is being grown to provide insights about its SPS planning and provide analytical solutions to help drive operational efficiencies, uncover the hidden risks and trends, reduce investigation errors and bad debt, improve customer experience and predict & recommend the optimizations for future state of SPS operations. As a Data Scientist, you will be responsible for modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic, data mining and visualization techniques, with a strong emphasis on leveraging Generative AI and Large Language Models (LLMs) to drive innovation. You will develop and deploy Gen AI-powered solutions for intelligent forecasting, automated pattern recognition in variance analysis, and conversational AI interfaces for operational dashboards. The role requires building agentic AI systems that enable natural language querying, automated root cause analysis, and intelligent recommendation engines for workforce optimization and resource planning. You will need to collaborate effectively with internal stakeholders and cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards. The candidate should be able to apply a breadth of tools, data sources and analytical techniques—including transformer architectures, foundation models, and prompt engineering—to answer a wide range of high-impact business questions and present the insights in concise and effective manner. Additionally, the candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high impact role with goals that directly impacts the bottom line of the business.