Careers

The AI Center of Excellence (AICE) builds AI primitives that power system-intrinsic intelligence and Trusted Intelligent Knowledge Infrastructure for both AI-as-a-Consumer and human users. We develop AI capabilities independently and in partnership with product-owning teams to evolve Amazon's AI-driven operations and productivity. Our impact is enterprise-broad and global. We are looking for an Applied Scientist who can identify high-impact problems, design rigorous experiments, and translate research into hardened, reusable primitives ready for broad consumption across the enterprise. Key job responsibilities * Research, design, and develop AI/ML primitives - defining problem spaces, formulating approaches, running experiments, and validating outcomes with scientific rigor * Translate research findings into production-ready primitives, working closely with machine learning engineers to harden and scale solutions * Drive the scientific direction of AICE primitives, identifying opportunities where novel approaches can unlock step-function improvements * Design and execute experiments to validate primitive performance, robustness, and generalizability before broad consumption by partner teams * Collaborate with product-owning teams to understand their problem spaces and ensure AICE primitives deliver measurable value when integrated * Contribute to AICE's mission of driving AI transformation through best practices in applied research, evaluation methodology, and responsible AI within your area of expertise * Publish findings, share learnings, and raise the scientific bar across the team A day in the life Every day brings new challenges. You might be deep in experimentation - designing evaluations to stress-test a new primitive's robustness. The next day, you could be analyzing results and iterating on model architectures. Later in the week, you may collaborate with partners to understand their domain and shape a primitive that fits their product needs. Other days, you'll work alongside our MLEs to harden a validated approach for broader consumption. No two weeks look the same. You'll move fluidly between research and applied problem-solving - always with the goal of building AI primitives that are scientifically sound, trusted, and impactful at enterprise scale. About the team AICE is a team of scientists and machine learning engineers building the AI primitives that power Amazon's intelligent systems. We develop and harden reusable capabilities for broad consumption by product teams across the enterprise - and partner with those teams to integrate them.

The Data Intelligence team is a new function within Amazon Customer Service. We own the end-to-end process of defining, building, implementing, and monitoring a comprehensive data strategy. We also develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences. As an Applied Scientist, you'll own the definition and implementation of customer-focused, AI-driven innovation in Amazon Customer Service globally, leveraging GenAI, ML, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With your expertise in AI, text analysis, embeddings, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This position will advance applied science best practices, leverage data and AI to drive customer experience improvements, and set new global standards for customer experience. This role requires you to work with a cross-functional team, including scientists, engineers, and product managers, to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technical skills in AI techniques (e.g., automated reasoning, reasoning, planning, knowledge representation), excellent written documentation skills, and experience with big data technologies. Success in this role requires combining deep business knowledge with hands-on technical skills to solve customer problems and address complex technical challenges. Key job responsibilities - Develop innovative solutions to complex problems (e.g., Automated Reasoning for Trusted AI-Enabled Customer Service). - Apply technical expertise to implement novel algorithms and modeling solutions, in collaboration with other scientists and engineers. - Analyze data and define metrics to identify actionable insights and measure improvements in customer experience. - Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and internal/external publications. - Collaborate with product management and engineering teams to integrate and optimize models in production systems. A day in the life A typical day as an Applied Scientist in the Data Intelligence team involves combining business expertise with hands-on problem-solving in ML and AI. The role encompasses tackling complex data initiatives, ensuring alignment with customer needs and business objectives, and translating business requirements into practical AI-driven solutions. Working collaboratively with cross-functional teams, this position involves designing and enhancing AI models, focusing on efficiency, precision, and scalability. Daily activities include ensuring data quality, monitoring model performance, and generating actionable insights from vast amounts of information. Each day presents opportunities to resolve complex technical challenges, advance important AI projects, and conceive innovative ways to leverage data in transforming the customer experience. About the team The Data Intelligence team is a new function within Amazon Customer Service. We develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! As an Applied Scientist, you will apply state of the art natural language processing and computer vision research to video centric digital media. We are looking for scientists with expertise in vision-language models/multimodal LLMs and long-form content understanding (full movies/episode vs. short clips). You will be dealing with architectures that handle long-context understanding and causal reasoning across extended temporal sequences. Key job responsibilities Our team builds multi-modal machine learning technologies to enrich and understand video content. We aim not only to understand individual components within the content itself, but also their relationships to each other to provide a holistic and broader contextual understanding. This powers the next generation of video understanding and search capabilities for Prime Video. About the team Prime Video's Content Localization, Understanding & Enrichment organization is responsible for 1) enabling Prime Video to "see" and "understand" video content including characters, scenes, dialogue, events & visual elements and 2) delivering localized, accessible content that meets a consistent cinematic quality standard at scale. This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.

Employer: Amazon Web Services, Inc. Position: Data Scientist II - AMZ27351.1 Location: San Francisco, CA Multiple Positions Available: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. (40 hours / week, 8:00am-5:00pm, Salary Range $175425 - $212800) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

Amazon Seller Assistant is our flagship GenAI-first, multi-agent system that reimagines Seller experience. Our vision is to provide each seller with a proactive, autonomous, agentic assistant that understands their business and helps them navigate the complexities of selling by anticipating their needs, surfacing insights, resolving issues, taking actions on their behalf, and helping them grow. Amazon Seller Assistant helps millions of sellers on Amazon serve billions of customers worldwide. We are seeking a world-class Senior Data Scientist to help define and build the next generation of Amazon Seller Assistant. You will partner with top-tier scientist, engineers and product teams to launch production-grade agentic capabilities at Amazon's scale — owning your problem space end-to-end, from a crisp customer insight to a shipped product that millions of sellers rely on. Key job responsibilities • Own the science vision, strategy, and roadmap for a key Seller Assistant capability area. • Define and ship agentic experiences — sub-agent onboarding, tool onboarding, evaluations— that solve hard seller problems at scale. • Partner with scientists and engineers to translate frontier AI research into production-grade features sellers trust and depend on. • Design rigorous evaluation frameworks — automated and human-in-the-loop — to measure agent quality, accuracy, and business impact. • Deep-dive into seller data, identify unmet needs, and write compelling PRFAQs that set the direction for your team. • Drive cross-functional alignment across science, engineering, UX, and business teams to deliver with speed and quality. About the team Amazon Seller Assistant team operates at the very frontier of agentic AI and agentic commerce — not as a research group, but as a team shipping production-grade, multi-agent systems used by millions of sellers worldwide. We move with the urgency of a startup and the resources of the world's most customer-obsessed company, the latest breakthroughs in science and engineering into capabilities that sellers rely on every day.

How to use the world’s richest collection of e-commerce data to improve payments experience for our customers? Amazon Payments Data Science team seeks a Data Scientist for building analytical solutions that will address increasingly complex business questions in the Amazon Currency convertor space. Amazon.com has a culture of data-driven decision-making and demands insights that are timely, accurate, and actionable. This team provides a fast-paced environment where every day brings new challenges and new opportunities. As a Data Scientist in this team, you will be driving the analytics roadmap and will provide descriptive and predictive solutions to the Amazon currency convertor business team through a combination of Gen AI, LLM and other machine learning techniques for text analytics, segmentation and prediction. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards. Key job responsibilities • Understand the applications of causal inference models on real datasets, including assessment of marketing campaigns, online experiments, uplift analysis etc • Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management • Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus are • Innovate by adapting new modeling techniques and procedures • Effective exploratory data analysis, and model building using industry standard regression and classification techniques such as Random Forest, XGBoost package, Keras framework • Demonstrate thorough technical knowledge Fine Tuning of Amazon LLMs to handle large blocks of text, using Generative AI to solve for summarization tasks and prevent catastrophic forgetting, feature engineering of massive datasets, • Be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets • Have exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive

Do you want to transform the way people shop at Amazon? We are looking for stellar applied scientists to be part of our multi-disciplinary team. We are re-imagining the future of the Amazon shopping experience for customers through tailoring it to their current intent. We understand what customers are interested in shopping for, and guide them to discover what they need as they shop. If you love building new technology that helps customers solve their problems, this is your opportunity to impact millions of customers. Come help us build the future of personalized shopping at Amazon! As an Applied Scientist on the team, you will work on science innovation in our space across a large multidisciplinary team. You will have a breadth of problems to work on, ranging from developing state of the art LLM-based techniques to reason about customers and products, developing deep learned transformer-based models to understand and abstract customer intent signals and representations, building large-scale real-time multi-task ranking systems, and more. You will build technology employed by teams across the company, while also having a direct connection to millions of customers through our own customer facing features every day. Come join us in the journey! Key job responsibilities Key job responsibilities * Deliver new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime * Be a key contributor on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers * Propose and innovate on the science roadmap across multiple projects About the team Our vision is to build the next generation of shopping experience at Amazon through personalization and understanding the customer's intent. We imagine our core experiences to work together as a talented personal shopping assistant would — a partner that is knowledgeable, understands your preferences, and helps you find the right solution for your needs. We aim for the quality of personalization to be a core reason customers choose Amazon, on par with Earth’s largest selection, low prices, and fast and free shipping. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for ...", “Customers who bought this item also bought”, and “Frequently bought together”. Amazon’s internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow.

The Amazon Search team's vision is to deliver high quality search results regardless of how customers phrase their search queries. Keyword-based search breaks down when confronted with natural language expressions. Queries like "I have ants in my house," "headphones comparable to Bose," "breakfast foods for someone avoiding sugar," and "scratch resistant flooring for dogs that looks like real wood" require world knowledge, common-sense reasoning, and sophisticated language understanding that customers increasingly expect. Core Search team is reimagining search architecture using Large Language Models (LLMs): a new LLM stack that already powers Amazon Search, Alexa+, Alexa for Shopping, Help Me Decide, Interests AI, confidential initiatives, and a growing portfolio of Amazon experiences across Stores and Devices. We build this stack as a primitive to supercharge a new generation of natural-language experiences across Amazon. We are hiring an Applied Scientist to push the science behind this stack: the reasoning LLMs, embedding models, cross-encoder rankers, and multi-objective optimization systems that turn billions of products into the right answer for hundreds of millions of customers. The role spans the full model lifecycle, from mid-training reasoning models on shopping data to aligning the models with customers on the dimensions that matter for shopping: helpfulness, trust, and faithfulness. You will build with us a natural language AI interface to billions of products, for all Amazon customers. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation across multiple problems and surfaces. You will: - Develop personalized multi-modal thinking-LLM techniques that reason about customers, queries, and products. - Mid-train and post-train large language models on shopping data: domain-adaptive continued pre-training, ireinforcement learning shopping reasoning traces, and instruction tuning for natural-language shopping queries. - Align models with customer interests on the dimensions such as helpfulness, harmlessness, and faithfulness. Apply Reinforcement Learning (RLVR, RLHF), Direct Preference Optimization (DPO), and customer-behavior-derived reward models. - Create semantic representations of products, customers, and context (bi-encoder embeddings, contrastive learning, hard-negative mining, cross-lingual training). - Develop cross-attentive LLM rankers that score candidate products against rich query intent and complex constraints. - Train multi-objective ranking and optimization systems that balance relevance, purchasability, and personalization. - Drive improvements on offline benchmarks as well as online experiments. About the team Core Search builds the next-generation LLM-powered retrieval and ranking stack for Amazon. We own the stack end-to-end including LLM models, personalization, multi-turn natural-language refinements, routing, the experimentation service, and the partner-facing primitive that other Amazon teams build on top of. The team is highly motivated, collaborative, technically deep, and runs with strong executive sponsorship and strategic visibility. In this role, you will define program strategy, prioritize investments, and shape how AI-driven natural-language search experiences ship across all devices, globally.

MULTIPLE POSITIONS AVAILABLE Employer: Amazon Development Center U.S., Inc. Offered Position: Applied Scientist III - AMZ007408 Job Location: New York, NY Position Responsibilities: Participate in the design, development, evaluation, deployment, and updating of formal reasoning systems for security, privacy, and data protection applications. Drive technical and scientific innovation in security automation, data protection, and privacy-preserving technologies, with a focus on developing scalable solutions for cloud environments. Develop and/or apply formal verification techniques and automated theorem proving methods for different applications in cloud security and privacy. Collaborate with internal and external users to understand requirements and enhance formal verification and automated reasoning capabilities. Lead research and development efforts in AI security, specifically evaluate emerging threats and opportunities, including securing Generative AI systems and designing robust safeguards. Proactively identify and explore new opportunities for deploying and leveraging formal reasoning solutions across various domains.

The AOP (Analytics Operations and Programs) team is responsible for creating core analytics, insight generation and science capabilities for ROW Ops. We develop scalable analytics applications, AI/ML products and research models to optimize operation processes. You will work with Product Managers, Data Engineers, Data Scientists, Research Scientists, Applied Scientists and Business Intelligence Engineers using rigorous quantitative approaches to ensure high quality data/science products for our customers around the world. As a Data Scientist, you will play a crucial role in supporting the team by creating and maintaining the data infrastructure necessary for the advanced analytics and machine learning solutions. Our team solves a broad range of problems that can be scaled across ROW (Rest of the World including countries like India, Australia, Singapore, MENA and LATAM). Here is a glimpse of the problems that this team deals with on a regular basis: • Using live package and truck signals to adjust truck capacities in real-time • HOTW models for Last Mile Channel Allocation • Using LLMs to automate analytical processes and insight generation • Ops research to optimize middle mile truck routes • Working with global partner science teams to affect Reinforcement Learning based pricing models and estimating Shipments Per Route for $MM savings • Deep Learning models to synthesize attributes of addresses • Abuse detection models to reduce network losses Key job responsibilities 1. AI Agent development for data analytics. 2. Analyze data with statistical and ML techniques. 3. Develop analysis/model in scripting languages (e.g. Python, R) and statistical/mathematical software (e.g. SAS, Matlab, etc.). 4. Develop science-based Supply Chain solutions. 5. Analysis/model documentation. 6. Learn and understand state-of-the-art statistical and ML techniques/tools. 7. Learn and understand Amazon Supply Chain operations.