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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 add-on subscriptions such as Apple TV+, Max, 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 technologist, 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! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Our team works at the intersection of generative recommendations, multi-objective reinforcement learning, and whole-page optimization. We're rethinking how recommendation systems construct experiences end-to-end, moving beyond ranked lists toward intelligent, adaptive page-level decision-making at scale.

Worldwide Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon's 19+ overseas marketplaces and supporting local Sellers' success and growth on Amazon. Our vision is to be the first choice for all types of Chinese business to go globally. The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools. The WWGS-AIT team is positioned to establish AI-ready foundational capabilities across the WWGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development. WWGS-AIT is looking for a Sr. Data Scientist to design and build seller-facing AI agents that turn our AI-ready data foundation into intelligent, conversational experiences for Amazon's global sellers. You will own the intelligence layer of these agents end-to-end, from modeling and retrieval to evaluation and launch, working alongside applied scientists, data engineers, and the Seller Assistant platform team to put trustworthy AI directly into sellers' hands. Key job responsibilities - Design, build, and iterate seller-facing AI agents (LLM-powered) that help Chinese sellers grow globally, reasoning over WWGS-AIT's AI-ready data foundation and knowledge base. - Develop the intelligence layer of agents: retrieval-augmented generation (RAG) over our knowledge management system, tool-use / function-calling orchestration, prompt engineering, and model fine-tuning or adaptation where needed. - Ground agent responses in standardized metrics and unified seller profiles to guarantee consistency and accuracy across agents; design and enforce guardrails that prevent hallucination and protect sensitive, compliance-restricted data. - Build rigorous evaluation frameworks (golden datasets, offline evaluation, and online experimentation) to measure and continuously improve agent quality, safety, and seller impact. - Develop seller-intelligence models (segmentation, entity resolution / One-ID, ranking and recommendation) that power personalized agent experiences. - Partner with WWGS Tech and the Seller Assistant platform team to productionize agents and tools (e.g., via MCP), defining the model and intelligence contract while engineering operates the runtime. - Collaborate with business, product, and cross-functional partners to translate seller pain points into agent capabilities and measurable business outcomes. - Stay current with advances in GenAI and agentic systems, and bring applied research into production.

We are seeking an Applied Science Manager to lead a new business unit on the GameLift team focused on creating AI and ML-based applications for the gaming industry. This leader will own the technical vision, scientific rigor, and end-to-end delivery of applied science initiatives that solve complex problems in machine learning, data science, and live-service gaming at scale. The ideal candidate operates at the frontier of AI research and deployment, translating ambiguous business opportunities into production-grade ML systems that generate measurable customer and commercial impact. This role demands a hands-on technical leader who can define and execute an applied science roadmap while managing and mentoring a high-performing team of builders, scientists and ML engineers. You will be responsible for upholding the highest standards of scientific excellence, including rigorous experimental design, disciplined model selection, and reproducible evaluation methodology, while maintaining the speed and inventive culture of a startup operating within a large organization. You will partner with engineering, product, and business stakeholders to bring AI-powered products from research through production deployment, meeting customer requirements and delivery timelines. The successful candidate will be equally comfortable debating the merits of deep learning architectures in a technical review as they are presenting a product roadmap to senior leadership, and will thrive in an environment where building something new from zero to one is the daily expectation. Key job responsibilities Define and execute the technical roadmap for applied science initiatives, balancing frontier research with production delivery requirements and customer timelines Lead rigorous model development processes including algorithm selection, offline evaluation, A/B testing design, and statistical significance assessment, ensuring every production decision is grounded in scientific evidence rather than intuition Architect scalable, reusable ML platforms and inference systems designed to serve multiple products without proportional increases in staffing or rebuild cycles, enabling the team to move fast across a growing portfolio Manage and develop a team of applied scientists and ML engineers, providing technical mentorship, career growth opportunities, and performance management while maintaining a high hiring bar Drive end-to-end AI deployment from research prototyping through production inference, owning latency, availability, and cost targets alongside model quality metrics Establish and enforce scientific standards across all team projects, including peer review mechanisms, documentation requirements, and reproducibility practices that ensure technical decisions withstand scrutiny Partner cross-functionally with product managers, software engineers, and business leaders to translate customer problems into well-scoped technical solutions with clear success criteria Maintain a builder roadmap that sequences product launches against customer commitments, managing dependencies and communicating tradeoffs to stakeholders when scope or timeline pressure arises Enable the team to operate with startup-level autonomy and speed of invention while maintaining the operational discipline required for production systems serving customers at scale Stay current with developments across the AI/ML research landscape, identifying opportunities to apply new techniques A day in the life Your morning starts with production system health checks: inference latency, model freshness, experiment dashboards. Mid-morning you lead a technical design review, challenging your scientists on model complexity tradeoffs and coaching toward disciplined, phased approaches that maintain rigor without sacrificing speed. After lunch you join a cross-functional sync with product and engineering to align on delivery milestones, working through scope tradeoffs when customer requirements shift. Late afternoon is for people leadership: one-on-ones focused on career growth, reviewing hiring scorecards to keep the bar high, and scanning recent research for techniques your team can apply next quarter. Every day blends science, product delivery, and team building.

Fulfillment by Amazon (FBA) is a service that enables sellers to outsource supply chain and fulfillment to Amazon and use Amazon's world-class science, technology, and logistics infrastructure to deliver billions of products from manufacturing hubs to customer doorsteps worldwide with fast delivery promise. The FBA organization is looking for a Principal Economist with expertise in economic and econometric modelling and demonstrated strength in market mechanism design to join our cross-domain group of economists, data scientists, applied and research scientists and scholars. As a lead economist, you will design markets and implement agentic systems that deploy supply chain and fulfillment resources to millions of heterogenous sellers. You will build causal inference models and experiments to evaluate policy impact on seller outcomes, and shape how our products evolve into trustworthy autonomous systems — collaborating with business and software teams to solve key challenges facing the worldwide FBA business. Such challenges include designing mechanisms to align sellers' decisions with customers' needs through better coordinating inventory, inbound, capacity, and fee. Successful operations enable sellers' businesses growth, while ensuring worldwide Amazon customers have access to the largest selection of products through FBA sellers. In doing so, you will shape the economics of Amazon's global fast delivery programs, including Sub Same Day Delivery and Quick Commerce, across North America, Europe, and emerging markets. We are looking for a seasoned economist who brings rigorous causal and structural thinking to traditionally operations research problems and who thrives in the ambiguity of defining the roadmap rather than receiving it. The successful candidate will have familiarity with modern GenAI methods for automation and rapid prototyping. Beyond individual contribution, you will set the long-term technical vision across work streams, and influence product managers, engineers, scientists, and senior leaders on high-judgment decisions and trade-off. You will raise the bar for the organization by establishing best practices, driving science culture, and mentoring junior economists and scientists. We value deeply technical people who deliver results incrementally and frequently in a fast-paced, high-energy and fun environment, and who are eager to learn new areas and develop themselves and their colleagues. Key job responsibilities • Design markets (e.g., auctions), incentive mechanisms (e.g. pricing), develop economic models and execute large-scale experiments to increase supply chain efficiency, to evaluate seller-facing policies, to induce proper seller actions, and to uncover new opportunities that improve customers and sellers’ outcomes. • Shape the economics of Amazon's fast delivery programs and FBA sellers’ product selection strategy (e.g., Sub Same Day Delivery and Quick Commerce) • Bridge economics and operations research by building economic frameworks for large-scale supply chain and fulfilment management problems. • Operate as a thought leader across the organization; collaborate with product managers, scientists, and software developers to incorporate models into production processes and • Influence senior leaders at VP-level on technical and business direction, and represent the science perspective. • Identify and propose new science investment areas to business leaders, shaping where the team focuses next. • Mentor and develop junior economists and scientists, and raise the technical bar for the broader science community. About the team Sellers play a vital role in Amazon's ecosystem, integral to our mission of offering the Earth's largest selection, lowest prices, and fastest delivery speed. FBA is an optional service that enables third-party sellers to outsource order fulfillment to Amazon, and leverage Amazon's world-class facilities to provide customers fast delivery promise. With commitment to taking on even more of the supply chain and operational complexities on behalf of our selling partners, Amazon now provides an end-to-end suite of supply chain services. This comprehensive solution empowers sellers to reliably transport products from manufacturing sites to customers worldwide. The FBA team is the core group in charge of warehousing, inventory management, fulfillment and pricing, and a diverse range of recommendation and agentic services for sellers, as well as building the autonomous internal resource management systems. We work to learn seller behavior, understand seller experience, build automated and trustworthy autonomous assistants to sellers, recommend right actions to sellers, design seller policies and incentives, and develop science products and services that empower sellers to grow their businesses. To do so, we build and innovate science solutions that leverage the right tolls across different fields including economics, operation research, machine learning, statistics, and data analytics. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, or Sunnyvale, CA.

Amazon Advertising is a fast-growing multi-billion dollar business that spans desktop, mobile, and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third-party publishers; and extends across US, EU, and an expanding number of international geographies. The Trusted Supply organization has the charter to safeguard advertiser trust and ensure high-quality ad impressions across all Amazon Advertising surfaces. We develop advanced algorithms and infrastructure systems to protect advertisers from unsafe content adjacency, low-quality inventory, fraud and privacy threats. Our scope spans a wide variety of problems in computational advertising including brand safety classification, content suitability scoring, risk hunting and proactive threat detection, viewability prediction, Made-for-Advertising (MFA) detection, malvertising identification, and privacy-preserving measurement and integration. We are looking for an exceptional Principal Applied Scientist to define and drive the science vision across Brand Safety, Suitability, and Risk Hunting as primary areas of focus, while contributing to broader Supply Quality challenges around viewability, privacy-preserving solutions, and data leakage prevention. This is a high-visibility leadership role where your models and systems will process billions of ad impressions daily, directly impacting advertiser confidence, customer experience, and a multi-billion dollar business. Key job responsibilities Set the science vision — defining multi-year research directions, establishing the publication roadmap, and driving innovations Operate across programs — influence modeling frameworks across brand safety, MFA detection, traffic quality, viewability, and 3P integrations; break down silos between science and engineering teams Act as a thought leader — anticipate industry shifts (privacy regulations, adversarial evolution, GenAI-powered threats), propose counter-strategies before they become critical, and represent Amazon in industry forums (TAG, MRC, IAB) Hire, mentor, and grow a high-performing team of applied scientists and research engineers; establish a culture of scientific rigor, peer-reviewed publications, and rapid experimentation Partner with engineering leaders to build efficient, scalable, low-latency production systems that serve models at billions-of-requests-per-day scale Influence product and business strategy — translate science capabilities into advertiser-facing products (targeting controls, transparency reports, quality guarantees) and quantify business impact

Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 3+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems

As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment. • Formulate fulfillment operations problems (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

The AWS Central Economics & Science team is looking for a PhD economist. The ideal candidate will be proficient in both reduced form and structural estimation and, most importantly, will be eager to learn new methods where applicable. The ideal candidate should be a problem-solver first, with an ability to bring theoretical frameworks to real-world business problems, working backwards from the business problem rather than from any particular solution method. In this role, you will become a subject-matter expert in cloud infrastructure, creating theoretical frameworks, data-driven insights, and statistical models to help AWS serve its total demand at a lower cost. You will work closely with finance, product, and engineering teams—as well as economists and other scientists—to understand complex systems and products, and will have the freedom to propose, explore, and deliver on a wide variety of projects that emerge from your research. Our team functions like a start-up within the AWS ecosystem—we have the freedom to identify greenfield problems that other economists have not explored yet, and build trust with the business through delivering valuable insights and policy changes. Most importantly, we solve problems at a massive scale and do it in a collaborative, curious, supportive, and fun environment. Key job responsibilities - Become a subject-matter expert in various areas of infrastructure, cost management, and transfer pricing. - Deliver insights that leads to policy changes through analysis, modeling, and theoretical frameworks. - Collaborate closely with non-economist business partners to communicate insights, implement solutions through production models, and develop a research agenda. About the team ACES works on high-impact projects for AWS service teams and leadership. This position will support the cost and transfer pricing optimization team to help AWS continue to scale its infrastructure efficiently in a fast-changing technological and competitive environment.

We are seeking an exceptional Applied Scientist, Seller Abuse Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to detect and prevent abuse of Amazon's catalog worldwide. You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and behavioral data to build detection systems that are ahead of evolving adversarial tactics. Key job responsibilities * Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale * Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels * Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience * Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas * Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness A day in the life Day-to-day you can expect to: - Explore datasets to understand predictors and patterns of abuse - Work with product, program, and engineering stakeholders to build solutions into production that will last - Identify new and emerging abuse vectors as abusers get more sophisticated - Use search, graph, computer vision, NLP, and anomaly detection methodologies to automatically detect abusive actions. About the team Seller Abuse Prevention detects abuse across 4 distinct spaces of abuse: catalog, review, financial risk, and discovery/competitor abuse. Seller Abuse Prevention is embedded in a team of scientists that tackle cross-spanning risk prevention problems. The team has expertise across graph networks, LLMs/agents, and fraud detection.

External job description Job summary Amazon Publisher Services (APS) helps digital publishers around the world build and grow thriving businesses. We provide services and advanced technologies to web, mobile app and advanced TV publishers of all sizes, including many of comScore’s global top 100, to help them monetize their content with demand from multiple programmatic buyers. Our server-side header bidding solutions are fast and reliable across devices, handling billions of queries per day, delivering ads in milliseconds. The result is more profitable advertising for publishers and more relevant ads for customers. As a Data Scientist on this team, you will: • Solve real-world problems by getting and analyzing large amounts of data, diving deep to identify business insights and opportunities, design simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIE's, and Product Managers. • Write code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems. • Apply statistical and machine learning knowledge to specific business problems and data. • Build decision-making models and propose solution for the business problem you define. • Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. • Analyze historical data to identify trends and support optimal decision making. • Formalize assumptions about how our systems are expected to work, create statistical definition of the outlier, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. • Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing. Internal job description The candidate would work with Product, Engineering, BIEs and Scientist across Supply and Demand organization to help make APS the best performing supply path for Amazon ads advertiser customers. They would spearhead efforts to conduct experiments alongside demand and measurement teams to identify optimal perfomance path for advertisers while improving APS Share of Wallet. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing.