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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.

The Prime Video Science team leverages the latest in machine learning and AI techniques combined with causal inference to bring scientific rigor to the biggest decisions in entertainment: what content to make, what to license, and where to invest. We build large-scale models that simulate how our global customer base responds to change, and we get to see that work shape what the business does. Prime Video is an industry-leading entertainment business and a critical driver of Amazon Prime subscriptions, contributing to customer loyalty and lifetime value. We're looking for a Data Scientist to help us design the experiments that ground our models in reality, evaluate the AI systems we build, and turn our model and experiment results into insights the business can act on. As a Data Scientist on this team, you will design and analyze experiments, build evaluations for AI systems, and run deep-dive analyses on our models, experiments, and customer data. You will work close to science: probing why a model behaves the way it does, pressure-testing results before they reach senior leaders, and, where it helps, building your own statistical and causal models. The candidate should have strong communication skills and the ability to translate complex, ambiguous analyses into clear findings for both technical and business audiences. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment. Key job responsibilities • Design and analyze randomized experiments that validate and calibrate our models and measure the impact of content and product changes. • Build evaluations and benchmarks for the AI systems the team develops and define what "good" looks like for them. • Run deep-dive analyses on model outputs, experiment results, and customer behavior to surface the story behind the numbers and catch issues before they reach stakeholders. • Apply statistical modeling, causal inference, and data analysis to answer business questions and inform major investment decisions. • Communicate findings to business, finance, engineering, and science stakeholders through clear written analyses and business-facing documents. About the team The Prime Video Science team is a multidisciplinary group of applied scientists, data scientists, economists, and engineers. We take on some of the hardest research questions in the business, and our work carries visibility up to the CFO/CEO level. We pursue ambitious research at the intersection of machine learning, AI, and causal inference, turning that research into innovations that improve customer experience and strengthen business profitability. Few science teams get to work on problems this hard and this impactful; if that combination excites you, we'd love to talk.

Do you want to define the multi-year science vision that transforms how millions of customers experience AWS products? Do you want to influence the AWS investment in Cloud and AI technology and see how your recommendations influencing AWS VP level decisions and driving the growth of AWS business? Do you want to push the boundaries of data science (e.g. statistical modeling, causal inference, econometrics, product growth analytics, and forecasting models) and democratize how AWS senior leaders access analytics insights using agentic analytics system? The AWS Analytics Engineering is at the forefront of leveraging cutting-edge AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their product and customer data. Our vision is to use data science methods to enable AWS product teams and business leaders to drive product and revenue growth and create personalized, optimized, and simplified product experiences to delight our customers. We are looking for a customer-focused Principal Data Scientist to lead and define the science strategy across AWS services. In this role, you will set the technical direction for ML-driven product analytics across AWS Compute (EC2), GenAI & Agents, Database & Analytics, and Storage (S3) organizations. You will partner directly with GMs, VPs, and senior product leaders to translate complex business challenges into innovative scientific solutions that directly influence AWS's top line and bottom line. You will analyze underlying product growth insights, understand product growth drivers, and anticipate business risks that need to be surfaced to leadership. As a Principal Data Scientist, you will be the technical thought leader who becomes a thought partner for senior leaders, hands on analyzing business trends and customer insights, drives cross-organizational decision alignment, and raises the bar for scientific rigor across the team. You will operate effectively in ambiguous environments, exercise strong business judgment on high-impact decisions, have high ownership and deep understanding of AWS business, and continuously push the frontier of data science applications at AWS scale. Key job responsibilities - Define and drive the multi-year science vision and data science roadmap for AWS product growth analytics across AWS Compute, Database & Analytics, Storage, AI/ML, and other organizations - Attend AWS WBR to answer critical and timely business and analytics questions to drive clarify on AWS’ product growth strategy - Influence senior leaders across multiple organizations by building mental models on AWS growth and anticipate growth risks that should be mitigated - Serve as the technical thought leader and strategic advisor to senior AWS leaders (GM/VP level), translating business objectives into high-impact scientific decisions and identify opportunities that drives overall AWS product and revenue growth - Establish best practices for decision science, including econometrics, statistical modeling, and causal methods - Invent, operationalize, and scale novel analytical frameworks and metrics that enable data-driven product growth and executive decision-making - Mentor junior decision scientists, setting the bar for technical quality through code reviews, design reviews, and hands-on guidance - Communicate findings, conclusions, and strategic recommendations to both technical and non-technical executive audiences through effective verbal and written communication - Identify and champion new science opportunities that expand AAE’s impact across AWS, building the case for investment and driving adoption A day in the life As a Principal Data Scientist in AAE org, you will shape the science strategy that underpins product decisions across multiple AWS organizations. You'll spend your time partnering with VPs and GMs to identify the highest-leverage data science opportunities, architecting novel ML solutions to complex product challenges, and mentoring scientists across the team. You'll drive alignment across cross-functional stakeholders, ensure scientific rigor in our most critical initiatives, and communicate insights that directly influence AWS product roadmaps and growth strategy. You'll balance long-term vision-setting with hands-on technical leadership, diving deep into model architectures and data pipelines when needed. About the team We are a team of scientists and engineers supporting AWS product leaders to make high-impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We come from diverse backgrounds in statistics, computer science, engineering, and business analytics. We specialize in the full end-to-end ML development process, including data ingestion, ETL, model development, and model deployment in production. We support data science needs across AWS EC2, Database & Analytics, and S3 teams using deep learning, graph neural networks, forecasting, reinforcement learning, causal inference, and more. High Impact Projects: We work on high-impact, high-visibility projects that directly influence AWS product roadmaps and senior leaders' decisions. Supportive Team Environment: We are proud of our supportive and inclusive team culture, we have each other's back during ups and downs. Work-Life Balance: We believe 80% of value comes from 20% of work, so we always prioritize our backlog ruthlessly based on business value. Learning Opportunity: Extensive opportunities to understand AWS business and leverage state-of-the-art AI/ML and cloud technology.

We are looking for a Principal Applied Scientist to drive the research and development of real-time multimodal conversational AI. You will operate across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior. You will be the expert in your area while contributing across the full model lifecycle — from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what's possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle. As a Principal Scientist, you will set the technical direction for your research area, influence the broader roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment from inception. Key job responsibilities Foundation Model Scaling - Build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training - Advance the scaling and efficiency of conversational modes, including the relationship between data, model size, and real time performance. - Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception - Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts Post-Training & Reinforcement Learning - Design and build reward models and reward functions for speech systems — capturing naturalness, fluency, conversational quality, and real-time responsiveness - Develop and apply reinforcement learning methods to shape conversational behavior — teaching models natural timing, responsiveness, and fluid interaction - Build the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation - Design evaluation frameworks that capture the quality dimensions unique to real-time conversation Real-Time Perception & Generation - Advance the team's capabilities in real-time perception - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets

What happens when you give AI the ability to remember? Not cached responses — real structured memory that compounds over time and transfers across contexts. We're building the science behind this, and we need researchers who want to own the problem end-to-end. This is a founding role on a new team. You won't inherit models or maintain someone else's pipeline. You'll define the research direction, run experiments at scale, and ship what works directly to production. Key job responsibilities As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for knowledge acquisition and retrieval. You will adopt or invent new machine learning and analytical techniques in the realm of information retrieval, knowledge representation, and large language models. Specific responsibilities include: 1. Design and implement novel approaches to knowledge extraction from heterogeneous, unstructured data sources at organizational scale. 2. Build retrieval systems that match intent to relevant knowledge across domains — solving the "right memory at the right time" problem. 3. Own the quality of memory generation: what to capture, how to structure it, when to surface it, and when to let it decay. 4. Run large-scale experiments using Amazon's compute infrastructure and massive real-world datasets. 5. Develop evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level. 6. Collaborate with engineers to move from research prototype to production system in weeks, not quarters. 7. Invent new approaches to temporal knowledge management — how memories age, conflict, and compound over time. 8. Publish and patent novel approaches to knowledge acquisition and retrieval at top-tier venues. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team We're a new team within Personalization, focused on a different kind of recommendation: not "what product should this customer see" but "what knowledge should this AI use right now." Same scale, same rigor, entirely new problem space. The science is at the intersection of information retrieval, knowledge representation, and LLM reasoning — and the right approach hasn't been established yet. The team values innovation and offers a safe place to try, fail, and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. We offer creative space with an entrepreneurial work environment focusing on customer obsession.

We're looking for a senior scientist to lead the research direction for a system that gives AI persistent, compounding memory. This is a new problem space — not recommendation, not search, not summarization, though it draws from all three. The right scientist will define what this field becomes. You'll own the scientific roadmap, run a research agenda with real-world deployment targets, and mentor junior scientists. The team is forming now. Your first week will involve scoping experiments, not reading onboarding docs. Key job responsibilities As a Senior Applied Scientist, you will own the scientific roadmap for personalization initiatives, identifying high-impact research directions and translating ambiguous problems into well-defined ML formulations. You will lead end-to-end systems spanning knowledge acquisition, retrieval, and reasoning. Specific responsibilities include: 1. Define the scientific roadmap for knowledge acquisition, representation, and retrieval at organizational scale. 2. Lead research on how AI systems should learn from experience — what to capture, how to generalize, when to forget. 3. Design evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level. 4. Own end-to-end research from problem formulation through production impact measurement. 5. Mentor Applied Scientists and establish scientific standards for a new team. 6. Partner with engineering leadership to translate research into architecture decisions that shape the product. 7. Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact. 8. Publish at top-tier venues and advance the state of the art in applied knowledge systems. About the team Born out of Amazon's Personalization organization, which pioneered personalization at internet scale. We're applying deep expertise in large-scale ML to a fundamentally new domain where the signal space, objective functions, and evaluation criteria are all open research questions. The team values innovation and offers a safe place to try, fail, and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. We offer creative space with an entrepreneurial work environment focusing on customer obsession.

We're looking for a senior scientist to lead the research direction for a system that gives AI persistent, compounding memory. This is a new problem space — not recommendation, not search, not summarization, though it draws from all three. The right scientist will define what this field becomes. You'll own the scientific roadmap, run a research agenda with real-world deployment targets, and mentor junior scientists. The team is forming now. Your first week will involve scoping experiments, not reading onboarding docs. Key job responsibilities As a Senior Applied Scientist, you will own the scientific roadmap for personalization initiatives, identifying high-impact research directions and translating ambiguous problems into well-defined ML formulations. You will lead end-to-end systems spanning knowledge acquisition, retrieval, and reasoning. Specific responsibilities include: 1. Define the scientific roadmap for knowledge acquisition, representation, and retrieval at organizational scale. 2. Lead research on how AI systems should learn from experience — what to capture, how to generalize, when to forget. 3. Design evaluation frameworks for a system where "quality" means something new — right knowledge, right context, right confidence level. 4. Own end-to-end research from problem formulation through production impact measurement. 5. Mentor Applied Scientists and establish scientific standards for a new team. 6. Partner with engineering leadership to translate research into architecture decisions that shape the product. 7. Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact. 8. Publish at top-tier venues and advance the state of the art in applied knowledge systems. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team Born out of Amazon's Personalization organization, which pioneered personalization at internet scale. We're applying deep expertise in large-scale ML to a fundamentally new domain where the signal space, objective functions, and evaluation criteria are all open research questions. The team values innovation and offers a safe place to try, fail, and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. We offer creative space with an entrepreneurial work environment focusing on customer obsession.

The Catalog Services Product Knowledge team is seeking an Applied Scientist for the Catalog Services organization. Our vision is simple: build AI systems that are capable of a deep product understanding, so we can organize and scale the catalog metadata (schema) for Amazon e-commerce catalog worldwide. This is a complex problem because the magnitude of products entities (attributes, values, constraints) to be modeled to cover all the Amazon products worldwide. You will work on initiatives (models, artifacts) aim to solve the problem of producing Catalog schema with less reliance on humans and deliver them into the Amazon production ecosystem. Your efforts will build a robust ensemble of ML and GenAI techniques that will scale our catalog artifacts with a high precision across countries and languages. The scientist will own investments in machine learning, natural language processing, GenAI, to solve real world problems at scale. The team's output affects the velocity at which we build product schema and support the largest e-commerce catalog and impact million of customers. The team builds solutions ranging from automatic generation of product metadata, classification of entities, validation of concepts against customer traffic, creation of agents solving complex tasks mimicking human decisions at high precision, etc; all these developments drive true understanding of products at scale. The ideal candidate has deep expertise in one or several of the following fields: Generative AI, Agents, LLMs, Web search, Applied/Theoretical Machine Learning, Deep Neural Networks, Classification Systems, Clustering, Natural Language Processing. S/he has a strong publication record at relevant academic venues and proven experience in launching products/features in the industry. Key job responsibilities - Formulate open research problems at the intersection of GenAI, multimodal reasoning, and large-scale information retrieval—defining the scientific questions that transform ambiguous, real-world catalog challenges into models applied to production with high-impact - Push the boundaries of models and agentic architectures by designing novel approaches to catalog understanding, schema inference, where the problem complexity (billions of products) demands methods that don't yet exist - Make frontier models reliable—advancing uncertainty calibration, confidence estimation, and interpretability methods so that frontier-scale GenAI systems can be trusted for autonomous catalog decisions - Own the full research lifecycle from problem formulation through production deployment, designing rigorous experiments, iterating on ideas rapidly, and seeing your research directly improve data and catalog operations - Shape the team's research vision by defining technical roadmaps that balance foundational scientific inquiry with measurable product impact - Represent the team in the broader science community, publishing findings, delivering tech talks, and staying at the forefront of GenAI, and agentic system research About the team The team's mission is to infer knowledge, understand, and derive product schema for all Amazon products entering the Catalog. The work is critical to power drive policies on how products will be merchandised, guide Selling Partners, inform models how to infer attributes. All this information drives the navigational Taxonomy, Search and Detail Page experiences, impacting million of customers. The scientist collaborates closely with teams across the organization and outside the Catalog (Search, Personalization, etc) that rely on this team's developments.

The Amazon Fulfillment Technologies (AFT) Science team is looking for an exceptional Applied Scientist, with strong optimization and analytical skills, to develop production solutions for one of the most complex systems in the world: Amazon’s Fulfillment Network. At AFT Science, we design, build and deploy optimization, simulation, and machine learning solutions to power the production systems running at world wide Amazon Fulfillment Centers. We solve a wide range of problems that are encountered in the network, including labor planning and staffing, demand prioritization, pick assignment and scheduling, and flow process optimization. We are tasked to develop innovative, scalable, and reliable science-driven solutions that are beyond the published state of art in order to run frequently (ranging from every few minutes to every few hours per use case) and continuously in our large scale network. Key job responsibilities As an Applied Scientist, you will work with other scientists, software engineers, product managers, and operations leaders to develop scientific solutions and analytics using a variety of tools and observe direct impact to process efficiency and associate experience in the fulfillment network. Key responsibilities include: * Develop an understanding and domain knowledge of operational processes, system architecture and functions, and business requirements * Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approach * Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges * Create prototypes and simulations for agile experimentation of devised solutions * Advocate technical solutions to business stakeholders, engineering teams, and senior leadership * Partner with engineers to integrate prototypes into production systems * Design experiment to test new or incremental solutions launched in production and build metrics to track performance A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Amazon Fulfillment Technology (AFT) designs, develops and operates the end-to-end fulfillment technology solutions for all Amazon Fulfillment Centers (FC). We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. The AFT Science team has expertise in operations research, optimization, scheduling, planning, simulation, and machine learning. We also have domain expertise in the operational processes within the FCs and their defects. We prioritize advancements that support AFT tech teams and focus areas rather than specific fields of research or individual business partners. We influence each stage of innovation from inception to deployment which includes both developing novel solutions or improving existing approaches. Resulting production systems rely on a diverse set of technologies, our teams therefore invest in multiple specialties as the needs of each focus area evolves.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: Santa Clara, California Job Number: AMZ9976173 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: $183,000/year to $247,600/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