Careers

Amazon Live is building the future of shoppable video — connecting brands with customers through livestreams, short-form video, and creator-driven content across Amazon Shopping, Fire TV, Prime Video, and social platforms. The product serves millions of monthly viewers, processes billions of events daily across 9 marketplaces, and generates tens of millions of dollars in advertiser revenue through self-service and managed channels with a goal to reach 100MM+ MAUs and 20K+ brands in the next couple of years. Amazon Live has a mature data platform powering reporting across 9 marketplaces, 14+ dashboards, and real-time creator analytics. The opportunity ahead is different: proving the causal value of video to brands, Amazon's programmatic systems, and product decision-making. This requires production ML models, data experimentation frameworks, and content intelligence that do not exist today — and that is exactly what this role builds. You will be one of the first scientists on this team — defining the measurement methodology, experimentation standards, and model architecture from the ground up. The problems are high-ambiguity, the data is rich, and the impact is visible — your models will directly influence how brands invest and how millions of customers discover content. Are you excited by the challenge of building causal measurement, content intelligence, and ranking signals from scratch for a product customers interact with daily? Do you want to own the full lifecycle from research question through production deployment, where your work moves multi-million dollar business decisions? We are looking for an Applied Scientist to join the DESA team and build production-grade models, experiments, and signals that close these gaps. You will own problems end-to-end — from framing the research question through model deployment and A/B experimentation — working alongside Data Engineers who build the infrastructure and a BIE who owns executive reporting. Your outputs will not sit in notebooks. They will run in production, feed downstream ranking systems, power brand-facing metrics, and give partner teams the evidence they need to prioritize integrations with Amazon Live. Key job responsibilities - Design and deploy causal attribution models (incrementality testing, multi-touch) replacing heuristic approaches, producing defensible numbers for partner teams and brand-facing ROI metrics. - Build brand lifecycle models (LTV, cost-to-acquire, adoption funnel) and campaign optimization models (marketing mix, diminishing returns) that scale self-service revenue. - Design and run A/B experiments with proper methodology (holdouts, pre-registration, power analysis) for new product surfaces, ranking changes, and attribution model transitions. - Build multimodal and generative models for content intelligence — extracting structured signals from video and producing scored creative assets at scale. - Develop ranking and personalization features (content affinity, creator quality indices, cross-session engagement patterns) consumed by downstream distribution systems. - Build predictive models proving video value to Amazon's programmatic systems where existing retail signals fail. - Own the full lifecycle from research question through production deployment, monitoring, and iteration. - Present findings and methodology to senior leadership (Director/VP) and partner teams, translating model outputs into business decisions. - Contribute to the science community through internal publications, reading groups, and cross-team methodology sharing. We value builders who thrive in ambiguity, move fast from hypothesis to production, and measure their success by business outcomes rather than paper count. A day in the life You will partner directly with product managers, monetization leads, and engineering peers to scope what to measure and how to prove it. Some weeks you will be designing an incrementality framework for ad lift and presenting methodology to leadership. Other weeks you will be training a multimodal model on broadcast video, packaging ranking features for the distribution team, or developing an A/B experiment for a new video produce on a new discovery surface. You will rapidly prototype and test hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgment. You will work on DE-built foundational data assets (content metadata, shopper profiles, retail/advertising integrations) and have access to a self-serve analytics agents and agentic-operational tooling that accelerate exploration. You will present findings to senior leadership (Director/VP level) and partner teams, turning data into prioritization decisions. About the team The DESA team owns the data platform, analytics, and sciences for Amazon Live and Shop the Show. The team's mission: quantify the value of live video to Amazon's ecosystem and make that value programmable — for brands deciding where to invest, for product teams deciding what to build, and for Amazon's systems deciding what to show customers. You will be one of the first scientists on a team that has built the data foundation and is now ready to build the intelligence layer on top. The platform processes event data from internal Amazon systems and social surfaces across 9 marketplaces. DEs own the infrastructure and foundational data assets. The BIE owns executive reporting. Applied Scientists own the models, experiments, and signals that turn raw data into product value and business decisions. Your outputs become shared org infrastructure — attribution models for partner teams, ranking features for distribution, experiment methodology for product, and content intelligence for creative supply.

Team & Project Overview The NBS Data Central team powers analytics, data science, and AI capabilities for Worldwide Global Selling (WWGS). We build scalable data products, and insight-generation systems that drive seller growth across 10+ marketplaces. Seller Intelligence is a P0 foundation theme at the Global Selling level, formed by merging "One Tagging" and "Good Contact" workstreams. It provides seller identity, segmentation, and contact-reach infrastructure that underpins all downstream seller-facing AI workflows — including intelligent outreach, personalized recommendations, and automated engagement. Scope of Impact Own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE) Directly impact seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions Models and data products consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC) Influence $100M+ annual seller GMS through improved segmentation and contact optimization Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.

Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams. Join our Recommendations team within Intelligent Talent Acquisition (ITA) where you’ll build machine learning products that transform how job seekers find opportunities and recruiters discover talent. You’ll develop sophisticated recommendation systems powering both Amazon Jobs and internal hiring platforms, operating at global scale to match the right people with the right positions. Using techniques including representation learning, reinforcement learning, and probabilistic modeling, your work will directly improve efficiency for recruiters and help candidates find their ideal roles. This position offers the chance to solve complex problems with significant impact by creating systems that make Amazon’s entire hiring ecosystem more effective while collaborating with scientists across the organization. Key job responsibilities - Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale. Our ideal candidate has a strong scientific foundation and experience of statistical analysis and model building and has a passion for fairness and explainability in ML systems. - Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience. - Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models. - Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability. - Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition. You will have the opportunity to provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve. A day in the life You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline. Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise. About the team The Recommendations team is a hybrid group of software engineers and applied scientists located in Edinburgh. We build tools that match people to jobs and jobs to people, optimizing experiences for both recruiters and candidates. Our work directly impacts Amazon’s ability to find and hire exceptional talent globally. The team maintains a collaborative environment with regular knowledge sharing and mentorship opportunities. We work closely with our product teams to understand business needs and develop innovative scientific solutions that improve hiring outcomes across both industry and student requisitions worldwide.

Employer: Amazon Web Services, Inc. Position: Applied Scientist II - AMZ27496.1 Location: Boston, MA Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $161803 - $193200) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

At Amazon we believe that Every Day is still Day One! We’re working to be the most customer-centric company on earth and Amazon's Central Reliability Maintenance Engineering (C-RME) team is at the heart of that mission, using science and data to drive scalable maintenance best practices across Amazon business units globally. We are seeking a Senior Applied AI Engineer/Scientist to lead key semantic layer and knowledge intelligence initiatives. This role sits at the intersection of knowledge engineering, ontology design, and applied AI, owning workstreams for the development of semantic foundations and dedicated science approaches that ensure their accuracy, consistency, and explainability in service of agentic and non-agentic AI across RME. Key job responsibilities In this role, you will contribute to the success of Central and Field RME teams working with new launches of Amazon buildings, as well as ensure that our Field teams benefit from state-of-the art AI solutions to support Global Operational Excellence. You will closely work with our team of senior scientists and systems engineers in our knowledge intelligence team, which is leading the full lifecycle of graph-based AI solutions, from customer problem formulation and ontology design to production deployment, enabling network-wide data discovery, decision support, and compliance monitoring. A core part of your mandate is to lead the semantic modelling and ontological foundation layer that supports both explainability and retrieval capabilities. This foundation feeds into transversal initiatives spanning multiple teams and products involving multiple AI approaches. You will closely work with Senior Applied Scientists owning explainability, causal reasoning, intelligent retrieval and question answering over knowledge graphs. As a Senior Applied AI Engineer/Scientist, you will: • lead the semantic layer for agentic AI, including developing and assessing the ontological foundations that enable autonomous workflows and cross-site best practice sharing. You will also disseminate governance and standardization practices that ensure downstream consumers (including retrieval and explainability systems) operate on consistent, well-defined semantics • design, build, and deploy graph-based AI solutions that combine knowledge graphs, Large Language Models (LLMs), and ML models to extract meaning from large-scale unstructured document collections, enabling data discovery, classification, and governance across RME • define and own knowledge pipelines that extract, transform, and enrich entity relationships from diverse unstructured and semi-structured sources into production-grade knowledge graphs, ensuring reliability and accuracy of the overall information architecture • collaborate with fellow senior scientists to design, deploy, and operate graph and vector databases to support retrieval, causal reasoning, and analytics use case as well as ensure contributing scientists maintain versioning, validation state, and provenance for every knowledge graph entry • collaborate with fellow senior applied scientists to ensure ontology and schema design decisions optimize for queryability, so that question-answering and retrieval systems can leverage the semantic layer with minimal impedance mismatch • integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, entity extraction, and semantic analysis, applying rigorous experimentation and evaluation methodology to select the best fit-for purpose approach • design ontological structures that support explainability, enabling agents and reasoning systems to trace reasoning paths and surface provenance, enabling governance-level transparency for autonomous AI actions • optimize models and inference pipelines for production constraints including latency, throughput, cost, and infrastructure reliability • establish best practices and standards for knowledge engineering and applied science processes, elevating the maturity of RME's data, information, and AI capabilities • mentor and train colleagues on knowledge graph concepts, semantic modelling, and applied AI techniques About the team The Amazon Reliability and Maintenance Engineering (RME) team maintains and optimizes technologies ranging from large, modern, purpose-built warehouses utilizing robotics and high-volume conveyance all the way through the value chain to small, high-speed warehouses placed as close to our customers as possible. Central Reliability Maintenance Engineering (RME) uses science and data to drive scalable maintenance best practices across Amazon business units globally. We do this to meet our customer promise, reduce costs, and support the Climate Pledge.

The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. The Search Relevance team focuses on several technical areas for improving search quality. In this role, you will invent universally applicable signals and algorithms for training machine-learned ranking models. The relevance improvements you make will help millions of customers discover the products they want from a catalog containing millions of products. You will work on problems such as predicting the popularity of new products, developing new ranking features and algorithms that capture unique characteristics, and analyzing the differences in behavior of different categories of customers. The work will span the whole development pipeline, including data analysis, prototyping, A/B testing, and creating production-level components. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world’s leading Internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Please visit https://www.amazon.science for more information Key job responsibilities Your responsibilities include but not limited to: * Analyze the data and metrics resulting from traffic into Amazon's product search service. * Design, build, and deploy effective and innovative ML solutions to improve search ranking. * Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production. * Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR.

Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life. We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS. We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers. Key job responsibilities · Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar. · Own and prioritize the team's science roadmap across data foundation, model development, and the delivery of insights into products and internal tools. · Set technical direction for the team's machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability. · Scope scientific projects, design and evaluate experiments, and ensure models are productionized and deliver measurable business impact. · Establish measurement, evaluation, and operational-excellence standards so model quality and impact are quantified and defensible. · Partner with product, engineering, design, and go-to-market teams to translate science into products and repeatable, scalable outcomes. · Communicate strategy, results, and trade-offs clearly to technical and non-technical leaders through written narratives and business reviews. · Foster a culture of scientific rigor, rapid experimentation, and operational excellence, and proactively identify and escalate risks with clear mitigation plans. About the team The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.

The Amazon Smart Vehicles (ASV) science team is seeking a passionate and skilled Applied Scientist with extensive expertise in advanced LLM technologies. This role involves innovating in rapidly evolving areas of AI research, focusing on creating personalized services to enhance drivers' and passengers' experiences. Your work will aim to simplify their lives, keep them informed, entertained, productive, and safe on the road, with direct application to prominent Amazon products. If you have extensive expertise in LLMs, natural language processing, and machine learning, along with experience in high-performing research teams, this could be the perfect opportunity for you. Our dynamic and fast-paced environment demands a high level of independence in decision-making and the ability to drive ambitious research initiatives through to production. You will collaborate closely with other science and engineering teams, as well as business stakeholders, to ensure your contributions are both impactful and delivered with maximum efficiency. Key job responsibilities - Leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI) - Work with talented peers to lead the development of novel algorithms and modeling techniques to advance the state of the art with LLMs - Collaborate with other science and engineering teams as well as business stakeholders to maximize the velocity and impact of your contributions About the team This is an exciting moment to lead in AI research and application. As part of the Amazon Smart Vehicles science team, you have the opportunity to shape the future by enhancing information-driven experiences for Amazon customers around the globe. Your work will directly influence customers through innovative products and services powered by language and multimodal technology!

Embark on an exciting science journey with International Emerging Stores Payments as we apply LLM techniques to improve the customer experience across multiple countries where we operate in. You will impact 1/4 of the world's population. Key job responsibilities 1. Translate ambiguous opportunities in to defined science problem statements 2. Evaluate and identify the right ML algorithm to solve for the problems. 3. Identify and build the right features for the Models 4. Partner with IML team in applying GenAI development strategies to accelerate model development 5. Publish papers to internal and external conferences.

We are looking for a Senior Applied Scientist to help drive the research and development of real-time multimodal conversational AI. You will contribute 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 own a significant research area and contribute 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 Senior Scientist, you will drive the technical execution of your research area, contribute to the team’s roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment. Key job responsibilities What You’ll Do Foundation Model Scaling - Help 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 models, 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 - Contribute to the state of the art on efficient architectures and training methods for conversational AI at scale 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 parts of 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 (latency sensitivity, audio quality, prosody, interaction naturalness) Real-Time Perception & Generation - Advance the team’s capabilities in real-time perception — the ability of the model to process incoming audio/speech while simultaneously generating responses - Develop techniques for natural interactive systems where the model handles concurrent input and output with human-like timing - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets