Careers

We are looking for a talented Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Own the model development lifecycle from experimentation through deployment — collaborating with software and ML engineers to ensure models meet latency, throughput, and reliability requirements at production scale Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As an Applied Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, collaborating with engineers to deploy computer vision models to production, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X, and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Role Join the Monetization team at Twitch, where we build the products that help creators make a living on the platform. You'll work on products like Subscriptions, Bits, and Gifting, and the pricing and packaging decisions behind them. You'll partner closely with product, engineering, finance, and data teams to measure the impact of new features, design and analyze experiments, and apply causal inference methods to inform decisions where A/B testing isn't possible. The work ranges from high-velocity experimentation on consumer-facing products to deeper pricing, policy, and segmentation analyses where causal identification is the central challenge. This role is well-suited for someone with a strong economics or causal ML foundation who wants to apply rigorous statistical thinking to real product decisions at scale. You'll need to be comfortable writing SQL, working with imperfect data, and partnering with stakeholders to turn analysis into product impact. Our team is based at Twitch HQ in San Francisco, CA. You can work in San Francisco, CA; New York, NY; or Seattle, WA You Will - Apply causal inference methods where experimentation isn't feasible - Develop models and analyses that inform pricing, segmentation, and revenue optimization - Design, run, and analyze A/B experiments - Partner with product, engineering, and finance to translate ambiguous business questions into measurement frameworks - Build and maintain dashboards, reporting, and analytical tooling that support ongoing decision-making Perks - Medical, Dental, Vision & Disability Insurance - 401(k) - Maternity & Parental Leave - Flexible PTO - Amazon Employee Discount

Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want—rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery—to make the leap from "searching for products" to "being understood as a person." As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every model you build ships directly to hundreds of millions of customers. The feedback loop between your science and real human delight is immediate. This role offers a rare combination of intellectual depth, technical ambition, and tangible impact. Come define what shopping looks like in the age of AI! Key job responsibilities - Innovate 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 leader 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 - Drive the science roadmap across multiple teams, helping coordinate a cohesive science agenda across the org. - Mentoring applied scientists across the org, growing their skills and careers. About the team Our mission is to delight every Amazon customer with a personalized shopping experience tailed to their intent. We achieve our mission through investments in Science, UX, and central systems with the purpose of delivering the future of shopping on Amazon. We are seeking a Principal Applied Scientist to lead the science charter across the recommendations and intent identification space.

We are looking for an Applied Scientist to join the Robotics Simulation team at Amazon Robotics. In this role you will design, build, and validate the simulation environments and policy training pipelines that enable robots to learn manipulation and mobility skills in simulation and transfer them to real hardware. You will work at the intersection of robotics simulation science and modern Physical AI: building GPU-accelerated RL environments, implementing imitation learning workflows, characterizing sim-to-real gaps, tuning physics parameters against real-world data, and evaluating learned policies both in simulation and on physical robots. You will collaborate closely with SDEs who build platform infrastructure, Technical Artists who create simulation assets, and partner science teams who consume your environments and pipelines for their model development. This is a hands-on, execution-focused role. You will own specific simulation science deliverables end-to-end, from environment design through policy evaluation, with increasing scope and independence over time. You will contribute to technical design discussions, propose improvements to the team's simulation fidelity and training methodology, and help establish best practices for robot learning in simulation. Key job responsibilities * Design and implement GPU-accelerated reinforcement learning and imitation learning environments in NVIDIA Isaac Lab for manipulation and mobility tasks. * Build and maintain policy training pipelines supporting diverse model architectures (diffusion policies, VLAs, behavior cloning, actor-critic RL) and evaluate trained policies in simulation. * Characterize and reduce sim-to-real gaps through systematic validation: compare simulated sensor outputs, kinematics, and dynamics against real-world robot data, then implement targeted improvements. * Implement domain randomization strategies (visual, physics, geometric) to improve policy robustness and transfer to real hardware. * Develop sim-to-real transfer techniques including system identification, physics parameter calibration, and visual domain adaptation. * Create robot embodiment validation tests (joint kinematics, actuator response, contact behavior) to ensure digital twins are faithful to real hardware. * Build data pipelines for recording, replaying, and augmenting demonstration data (from teleoperation or automated trajectory generation) to scale training data volume. * Contribute to end-effector modeling and contact dynamics tuning, ensuring physically plausible gripper and tool interactions in simulation. * Author design documents for new simulation science capabilities and contribute to technical reviews. * Collaborate with partner science teams to understand their model architectures and ensure simulation environments meet their training requirements. 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 The Robotics Simulation team is a multidisciplinary organization of SDEs, Applied Scientists, and Technical Artists at Amazon Robotics. We build the simulation infrastructure that powers Physical AI development, from photorealistic synthetic data to GPU-accelerated training environments. Our simulation stack enables robots to be designed, trained, and validated entirely in simulation before physical hardware exists, compressing development timelines and de-risking robotics programs across Amazon. The team delivers end-to-end simulation stacks for Amazon's robotics programs, including high-fidelity robot digital twins, teleoperation data collection infrastructure, scalable synthetic demonstration generation, policy training and inference pipelines (RL, imitation learning, VLAs), domain randomization for sim-to-real transfer, and model validation in simulation. We partner closely with hardware teams, science organizations, and robotics program leads across Amazon Robotics.

We are Amazon's central Responsible AI team. Our mission is to advance the science and practice of Responsible AI (RAI) to enable Amazon's tens of thousands of builders to build and deploy AI solutions to the high standards that our customers and society expect. As a scientist on this team, you will: - understand in depth the technical and scientific issues related to RAI, including controllability, security, privacy, safety, veracity, robustness, fairness, explainability, transparency, and governance - help define the strategies, priorities and metrics for RAI - solve open problems in RAI to unblock traditional, generative and/or agentic AI use cases and solutions, publishing as appropriate - help develop our team - liase with internal and external stakeholders, including the academic community, on issues related to RAI About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Ring & Blink is looking for a Senior Applied Science Manager to lead the AI development. In this role, you will be the leader of our passionate, talented, and inventive scientists, to develop industry-leading Multimodal AI, Research Infra, and scaling them successfully to production for the benefit of millions of Amazon Devices users. This is a unique, high visibility opportunity for a leader who wants to have business impact, and dive deep into computer vision problems. We are particularly interested in candidates with experience productizing edge-based computer vision systems. Key job responsibilities As a Senior Manager, Applied Science, you bring structure to ambiguous business problems and use science, logic, and practical experience to decompose them into straightforward, scalable solutions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems; you're interested in learning; and you acquire skills and expertise as needed. The ideal candidate is a strong, creative and highly-motivated Scientist/Engineers with hands-on experience in leading multiple research and engineering initiatives. You balance technical leadership with strong business judgment to make the right decisions about technology, tools, and methodologies.

We are looking for an Applied Scientist to join our team and tackle some of the most challenging traffic engineering problems at planetary scale. You will develop novel optimization algorithms and ML models that balance network utilization, resilience, cost efficiency, and latency — across traffic classes with fundamentally different characteristics. The problem then expands to network capacity planning, where you will collaborate with other scientists to develop the optimal strategy to efficiently scale the network. Key job responsibilities - Formulate traffic engineering problems as mathematical optimization models and develop scalable solvers that operate on graphs with hundreds of nodes and O(10^4) edges - Design ML models for traffic demand prediction, anomaly detection, and workload classification - Develop capacity planning frameworks that co-optimize cost, reliability, and performance over multi-year horizons - Build simulation and evaluation frameworks to validate TE algorithms against realistic failure scenarios and traffic patterns - Publish research at top venues (SIGCOMM, NSDI, INFOCOM, NeurIPS, ICML) and contribute to the scientific community - Collaborate with network engineers, software engineers, and operations teams to bring algorithms from prototype to production at global scale - Define metrics, run A/B experiments, and measure the real-world impact of algorithmic changes on network performance About the team The Inter Data Center (InterDC) Networking team owns traffic engineering across AWS's regions — the largest inter-datacenter network in the world, connecting hundreds of data centers across dozens of regions. We design and implement the algorithms, optimization systems, and scientific frameworks that decide how exabytes of traffic traverse this network every day. Our work sits at the intersection of combinatorial optimization, machine learning, distributed systems, and network engineering.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities - Define and lead science initiatives from problem framing through production deployment in a high-ambiguity environment - Develop and deploy models spanning computer vision, language, and search and retrieval that operate on multimodal inputs at scale - Design and analyze large-scale online experiments to measure impact on shopper and advertiser outcomes - Collaborate with engineering, product, and design to ship science into production A day in the life As an Applied Scientist on the Sponsored Videos team, you will tackle problems at the intersection of computer vision, generative AI, search and retrieval, and personalization. You'll own the full science lifecycle from research and experimentation through online testing and production deployment, working closely with engineering, product, and design partners to bring ideas to market. You should be comfortable working with multimodal signals, building models that operate at scale, and measuring impact through rigorous experimentation. Your work will directly influence the experience of hundreds of millions of shoppers and the outcomes of tens of thousands of advertisers. About the team The Sponsored Videos team within Sponsored Products and Brands develops the science and systems behind video advertising experiences that connect advertisers and shoppers across Amazon. We are on a mission to make Amazon the best-in-class destination for shoppers to discover, engage with, and build affinity with brands through videos.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Sponsored Products Search Sourcing Science (SPSSS) team's mission is to retrieve all relevant sponsored products in response to shopper queries, serving billions of daily ad impressions and tens of millions of clicks, helping shoppers discover useful and contextually relevant products while enabling advertisers to reach the right shoppers in the right context. To achieve this, we build state-of-the-art capabilities spanning query, shopper, product, and advertiser understanding, as well as advanced retrieval, targeting, and ranking systems, all powered by efficient large-scale data pipelines, deep learning, natural language processing (NLP), generative AI, and multi-agent workflows. It's a high-impact, technically exciting space where science directly translates into measurable outcomes for hundreds of millions of customers and millions of advertisers. Key job responsibilities As a Senior Applied Scientist on this team, you will: - Serve as the technical leader in Machine Learning and Generative AI, driving efforts within this team and across other teams. - Lead end-to-end ML projects with high ambiguity, scale, and complexity—from problem definition to production. - Build, optimize, and deploy ML models into production, partnering with software engineers to productionize solutions. - Establish scalable, automated processes for data analysis, model development, validation, and serving. - Apply strong knowledge of LLMs (prompt engineering, fine-tuning, RAG, evaluation) to build production-grade GenAI applications. - Analyze large-scale data sets to develop insights that increase traffic monetization and merchandise sales without compromising the shopper experience. - Design and run A/B experiments, and perform statistical analysis to measure impact and guide decisions. - Research and prototype innovative ML and GenAI approaches, bringing state-of-the-art techniques into production. - Recruit, mentor, and grow Applied Scientists on the team. About the team 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. The Sponsored Products Search Sourcing Science (SPSSS) team's mission is to retrieve all relevant sponsored products in response to shopper queries, serving billions of daily ad impressions and tens of millions of clicks, helping shoppers discover useful and contextually relevant products while enabling advertisers to reach the right shoppers in the right context. To achieve this, we build state-of-the-art capabilities spanning query, shopper, product, and advertiser understanding, as well as advanced retrieval, targeting, and ranking systems, all powered by efficient large-scale data pipelines, deep learning, natural language processing (NLP), generative AI, and multi-agent workflows. It's a high-impact, technically exciting space where science directly translates into measurable outcomes for hundreds of millions of customers and millions of advertisers.

We are seeking an exceptional Applied Scientist to join our JP Seller Services team, where you will reimagine how science analysis and modeling are conducted across the organization through an AI-native approach. In this role, you will design and build intelligent systems that enable any team member to validate business hypotheses with scientific rigor in hours rather than months. You will architect production-grade platforms spanning multi-agent AI frameworks, causal inference automation, generative AI, and simulation engines that democratize advanced analytics at scale. Your work will fundamentally transform how the teams generate, test, and deploy data-driven recommendations, scaling rigorous science solutions for every decision-maker to solve customer problems. The ideal candidate combines deep expertise in scientific analysis such as causal inference, machine learning, and AI system design with the vision to rethink the entire science lifecycle from hypothesis to deployment. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities - Lead the design and development of AI-native science platforms that automate the end-to-end lifecycle from hypothesis formulation through causal analysis, model validation, and deployment into production systems. - Design and build shared knowledge infrastructure (feature stores, experiment registries, model leaderboards) that enables cumulative organizational learning, where every validated insight accelerates future analyses. - Design and implement evaluation frameworks, including Seller simulations, that enable teams to validate model quality and test interventions against synthetic populations before live deployment. - Drive integration with downstream systems to close the gap between validated insights and seller-facing actions, ensuring science outputs reach the people and systems that serve customers. - Collaborate with cross-functional partners (product managers, category leaders, marketing managers, economists, and data scientists) to identify high-impact business problems and translate them into scalable scientific solutions.