Careers

Amazon's Worldwide Grocery Stores (WWGS), Data & Science team is seeking an Applied Scientist to join our under the roof (UTR) Science team, focused on improving outbound pick efficiencies across the Amazon Grocery Network. In this role, you will build optimization and simulation models that directly reduce operational costs and improve associate productivity in warehouse picking operations. This role owns the development and deployment of mathematical optimization models for pick planning, inventory placement, and warehouse layout design. You will formulate ambiguous business problems as concrete scientific models, develop and deploy production-grade solutions, and work closely with engineering partners, product owners, and business stakeholders to deliver measurable impact. Because UTR operations are complex and inter-connected (e.g., inbound stow vs. outbound pick), this role requires a strong understanding of these relationships and the ability to make trade-offs at the system level. You will interface directly with non-technical product owners and business leaders, manage expectations, and take an active part in influencing the feature roadmap. Key job responsibilities - Design, develop, and deploy mathematical optimization models (e.g., Mixed Integer Programming, meta-heuristics) to improve outbound picking efficiency, including pick planning and inventory placement. - Build simulation models to evaluate warehouse layout designs, test optimization solutions offline, and answer strategic what-if questions. - Formulate complex, ambiguous business problems into well-defined scientific solutions with clear objectives and constraints. - Collaborate with engineering teams to productionize models, establish data pipelines, and create scalable architectures. - Track solution performance post-deployment, identify issues through deep dives, and iteratively improve model quality. - Communicate technical concepts clearly to diverse stakeholders — scientists, engineers, product managers, and business leaders — through documentation, presentations, and design reviews. - Author peer-reviewed research papers on developed models and contribute to the internal scientific community.

Are you a PhD interested in machine learning, natural language processing, computer vision, automated reasoning, robotics, or quantum technologies? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Key job responsibilities Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.

With AI reshaping every layer of the software stack, we have an opportunity to reimagine consumer software experiences — making them smarter, more personal, and more useful every day. Join us in revolutionizing how millions of customers discover and interact with the web. The Silk team is building the next evolution of browsing - one that transforms passive web consumption into an intelligent, AI-powered experience that understands and anticipates user needs. About the Role As a Senior Applied Scientist on the Silk team, you'll be at the forefront of defining and building the future of web browsing in the AI era. This is a unique opportunity to shape both the scientific vision and product direction of a next-generation browser that will fundamentally change how customers shop, discover content, and engage with the web. You'll have the freedom to innovate across multiple advanced domains including: - Large language models and generative AI for intelligent browsing assistance - Advanced recommender systems for personalized content discovery - Natural language processing for deep web content understanding - Novel AI applications that transform traditional browsing paradigms Why This Role Matters The browser is one of the most fundamental tools of the modern internet, yet the core browsing experience has remained largely unchanged for decades. At Silk, we're reimagining what a browser can be by integrating advanced AI capabilities that make web interactions more intuitive, efficient, and valuable for our customers. As a senior scientific leader, you'll have unprecedented opportunity to: - Define and own the technical vision for AI-powered browsing experiences that impact millions of users - Pioneer new approaches to web interaction that combine browsing with intelligent assistance - Build novel solutions that bridge content discovery, shopping, and entertainment - Drive innovation in areas like intelligent content summarization, shopping assistance, and AI-powered browsing tools - Shape the future of how customers interact with the vast knowledge and capabilities of the web Impact and Leadership This role combines scientific leadership with product ownership and technical execution. You'll: - Lead the definition and development of Silk's AI strategy and roadmap - Drive technical decisions that shape our product direction - Mentor and grow other scientists while building a culture of innovation - Collaborate with product and engineering leaders to bring scientific innovations to life - Influence the broader direction of Amazon's AI initiatives Key Responsibilities - Conceptualize and lead innovative research in ML and AI that transforms web browsing - Design scalable AI solutions that power next-generation browsing experiences - Guide technical approaches for complex ML projects while working with cross-functional teams - Drive deep data analysis to uncover customer insights and identify new opportunities - Work closely with engineering teams to bring ML innovations to production - Lead improvements in model scalability, efficiency, and automation - Stay at the forefront of ML research and apply relevant innovations - Contribute to Amazon's scientific community through publications and knowledge sharing Why You'll Love It You'll work on challenging problems at massive scale while having direct impact on how millions of customers interact with the web daily. Our team combines the innovative culture of a startup with the resources and scale of Amazon. You'll have: - End-to-end ownership of scientific solutions that power next-generation browser experiences - Freedom to experiment with new ideas and approaches - Access to vast computational resources and unique datasets - Opportunity to work with and learn from world-class scientists and engineers - Direct influence on product strategy and technical direction - Platform to publish research and contribute to the scientific community Join us in building the future of intelligent web browsing that combines AI innovation with customer-centric experiences. This is your opportunity to reimagine one of computing's most fundamental tools and shape how the next generation discovers and experiences the web.

In this role, you will design and build intelligent multi-agent systems that automate root cause analysis for advertising campaign delivery at scale. You will architect agentic orchestration patterns where specialized sub-agents (campaign diagnostics, deal-level troubleshooting, pacing control) are invoked as composable tools by a reasoning layer that determines which subsystems to query based on the nature of the issue. You will develop hierarchical analysis frameworks that move from daily trend detection to intra-day anomaly isolation, enabling the system to pinpoint when and why delivery degraded rather than relying on static time windows. You will build self-learning feedback loops where the system identifies recurring failure signatures (auction dynamics, pacing anomalies, supply contention), updates its diagnostic knowledge as engineering teams deploy fixes, and retires stale patterns automatically. We are looking for a passionate Applied Scientist with technical expertise in LLM-based agent architectures, retrieval-augmented generation, time-series anomaly detection, and production ML systems. In addition to hands-on experience building agentic AI solutions, an ideal candidate should demonstrate the ability to translate complex distributed system behaviors into structured diagnostic reasoning, show a willingness to push the boundaries of how LLMs interact with real-time operational data, and thrive in an environment where you ship production systems that directly reduce advertiser escalation time from days to minutes. Key job responsibilities * Conduct deep data analysis to derive insights for the business, identify gaps, and uncover new opportunities. * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Run regular A/B experiments, gather data, and perform statistical analysis to optimize advertiser experiences. * Collaborate closely with software engineers to deliver end-to-end solutions into production. * Enhance the scalability, efficiency, and automation of large-scale data analytics, model training, deployment, and serving. * Research and implement new machine learning models and techniques to improve advertising performance. A day in the life Your primary focus is building a multi-agent diagnostic system that automates root cause analysis for advertising campaign delivery issues. On a typical day, you might review how the system handled recent escalations, identify where it reasoned incorrectly, adjust orchestration logic, and write new evaluation cases. You will design agent architectures that invoke specialized sub-agents as tools, build hierarchical analysis frameworks that move from trend detection to anomaly isolation, and develop self-learning loops that keep the system's diagnostic knowledge current as the underlying platform evolves. You will work closely with SDEs building the diagnostic platform, product managers defining the troubleshooting experience, and the support teams who rely on your system to resolve advertiser delivery issues in minutes instead of days. Beyond the core agent work, you may find yourself diving into causal inference to measure recommendation effectiveness, prototyping proactive anomaly detection, or contributing to evaluation science for systems that reason over complex operational data. About the team The Demand Enablement, Product Analytics and Operations team builds the diagnostic and intelligence layer for Amazon DSP, the demand-side platform powering Amazon's programmatic advertising business. We own the systems that detect, diagnose, and surface delivery issues across campaigns, giving internal teams and advertisers the visibility to act before problems impact spend. Our product portfolio spans automated troubleshooting platforms, advertiser-facing delivery insights, and AI-powered root cause analysis using multi-agent architectures on foundation models. We are a small, high-ownership team that ships production systems end-to-end, from data pipelines processing billions of bid events to LLM-based agents that reason over complex advertising systems. If you want to work at the intersection of applied science, distributed systems observability, and real business impact measured in advertiser dollars recovered, this is the team.

We are seeking a Research Scientist to join the SAF Lab. In this role, you will develop the core Control Barrier Function (CBF) theory and algorithms that form the mathematical foundation of the universal safety layer. Key to this process is a feedback loop between theory and practice: developing theory that is deployed on next generation robots and using experimental evaluation to drive new theory. This will enable you to push the boundaries of CBF theory: layered safety filters and trade-offs between robustness and optimality. A key challenge will be to understand the interplay with CBF theory and learned control policies, constructing safety filters that internalize learned policies and utilizing CBFs in learning to internalize safety. You will work with the inventor of control barrier functions and a team contributing directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet. Key job responsibilities • Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope highly dynamic robots • Frame safety filtering within complex layered architectures involving learning-based components, including VLAs, RL-based locomotion and whole-body controllers • Design multi-layer CBF based safety filters, including decision making layers, MPC, and real-time nonlinear feedback control elements • Formalize the interplay between models used in the CBF safety filter and the full order dynamics of the robotic systems, establishing formal guarantees even if the full order system dynamics is not known and contains learning-based elements • Understand the role of perception and semantic representations in the synthesis of CBFs, and the interplay between CBFs • Characterize the trade-offs between optimal safety and robustness to sensor noise, perception error, actuator and sensor failure • Address the theory-to-practice gap by developing CBF methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency • Implement real-time optimization solvers (e.g., QP-based safety filters) that execute within the tight timing budgets of safety-critical control loops • Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins • Contribute to the theoretical foundations of CBFs through publications at top-tier controls and robotics venues • Collaborate with perception, planning, locomotion, and manipulation teams to ensure CBF formulations accommodate the needs of upstream and downstream systems • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots) 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 Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, quadrupeds, and humanoids. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin Amazon's path to millions of robots operating alongside people.

Amazon Web Services (AWS) Applied AI Solutions (AAIS) is on a mission to make AI real for enterprises. We build and deploy production AI solutions that drive measurable business outcomes at scale, bringing together applied scientists, AI architects, business development professionals, and GTM specialists to help customers move from AI experimentation to production impact. Within AAIS, the GTM Acceleration team activates the field, measures impact, and scales what works. We are the connective tissue between AAIS product and science teams and the worldwide field organization, ensuring our AI solutions reach customers effectively, that we quantify the value we deliver, and that we build repeatable motions that scale globally. We are looking for an Applied Scientist who will serve as a force multiplier across our customer engagement teams, building the analytical foundations, predictive models, and reusable tooling that power our go-to-market strategy. You will work at the intersection of data science, machine learning, and business strategy, building models that quantify our value proposition, and creating scalable analytical assets that accelerate every engagement. This is a highly visible, high-impact role where your work directly influences how we demonstrate and measure the value of AWS AI solutions for enterprise customers. You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with software engineers, product managers, and business stakeholders. You will identify the right methodology for each problem, whether that is a classical statistical approach, a modern deep learning technique, or a novel combination, and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect Customer initiatives and across the Applied AI solution portfolio, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide. If you thrive at the intersection of rigorous science and customer-facing impact and are energized by translating complex model outputs into business decisions, we want to talk to you. Key job responsibilities Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements, business decisions, and customer outcomes Work directly with customers during production pilots to build and deploy AI solutions that demonstrate measurable business value Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes Build ROI models, business case tools, and forecasting systems for demand prediction, capacity planning, workforce optimization, and value quantification Apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale, and partner with software engineers to productionize models with reliability, monitoring, and operational excellence Build and own customer analytics capabilities including segmentation (by size tier, AI adoption, product penetration, entitlement), usage trend analysis, propensity modeling, and foundational datasets combining service usage with sales data Create self-service analytics platforms and automated insight delivery mechanisms that enable leadership to pull strategic intelligence on demand Enable field teams with reusable analytical assets, diagnostic notebooks, benchmarking studies, and scalable tooling that accelerate customer engagements Own success metrics and create mechanisms to measure model performance, adoption, and business impact across customer cohorts Define strategic frameworks and GTM recommendations by segment, translating data patterns and market signals into actionable go-to-market motions and investment priorities Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations, operating as a shared resource across 2-3 teams simultaneously About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & 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, mentorship 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences

Amazon Braket is investing in fault-tolerant quantum computing capabilities. We are looking for a Senior Applied Scientist with deep expertise in quantum error correction to work on compilation science as part of a team of scientists and engineers building fault-tolerant quantum capabilities. In this role, you will make design choices that directly influence production systems, working alongside the FTQC Science Lead to translate research direction into implementable solutions: which error correction approaches to pursue, how to map logical circuits to physical qubits, how to optimize resource usage, and how to integrate decoders into execution flows. You will work at the boundary of science and engineering, where your research directly informs what gets built. This is not a purely theoretical role. You will implement your ideas, benchmark them against real hardware constraints, and iterate with software engineers who translate your designs into scalable infrastructure. We are particularly interested in candidates who have taken QEC research from theory into implementation, whether in simulation or on physical hardware. Key job responsibilities - Drive scientific design decisions for fault-tolerant quantum workloads: error correction code selection, logical gate synthesis, and qubit mapping strategies - Develop and implement resource estimation algorithms that guide compilation optimization - Collaborate with software engineers to translate QEC research into production software - Benchmark approaches against realistic hardware noise models and device constraints - Work with quantum hardware providers on compilation strategies tailored to specific architectures - Publish research in coordination with the broader Braket science team, representing Amazon Braket at relevant conferences and workshops

Amazon Braket is investing in fault-tolerant quantum computing capabilities. We are looking for an Applied Scientist to own resource estimation and workload benchmarking for fault-tolerant quantum workloads on AWS. You will answer the fundamental questions: how many physical qubits are needed, what gate depths are achievable, and what error budgets are realistic for a given algorithm on a given device. Your models will inform technical decisions, customer conversations, and our roadmap. This role requires more than resource estimation methodology alone. You need a broad foundation in quantum error correction research to reason about the full picture: how code choices affect resource requirements, how logical circuit structure impacts physical costs, and how benchmarking results feed back into the system. You will be part of a small team of scientists and engineers, and we expect you to codify your solutions in production-quality code and contribute directly to the codebase alongside your teammates. Key job responsibilities - Build and maintain FTQC resource estimation models that determine qubit counts, gate depths, and error budgets for target algorithms - Develop benchmarking frameworks that evaluate compilation quality against realistic hardware constraints - Produce resource estimates that inform technical decisions and feed into customer readiness work led by the Applications & Engagement team - Collaborate with the QEC compilation scientists on how resource estimates feed back into code selection and optimization - Connect benchmarking outputs to published materials in coordination with Braket's science and product teams - Stay current with the rapidly evolving QEC literature and incorporate new results into estimation models

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Amazon Music Search Science team is seeking an experienced Applied Scientist who will join a team of experts in the field of machine learning, and work together to break new ground in the world of understanding and classifying different forms of music, and creating interactive experiences to help users find the music they are in the mood for. We work on machine learning problems for music classification, recommender systems, dialogue systems, NLP, and music information retrieval. You'll work in a collaborative environment where you can pursue applied research, with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the experience of Amazon Music customers on Alexa/Echo, mobile, and web. Key job responsibilities - Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes - Design, development and evaluation of AI models for predictive learning - Work closely with software engineering teams to drive model implementations and new feature creations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Research and implement novel machine learning and statistical approaches About the team Everyone on our team has a meaningful impact on product features, new directions in music streaming, and customer engagement. We are looking for new team members across a variety of job functions including software engineering/development, marketing, design, ops and more. Come join us as we make history by launching exciting new projects in the coming year.Our team is focused on building a personalized, curated, and seamless music experience. We want to help our customers discover up-and-coming artists, while also having access to their favorite established musicians. We build systems that are distributed on a large scale, spanning our music apps, web player, and voice-forward audio engagement on mobile and Amazon Echo devices, powered by Alexa to support our customer base. Amazon Music offerings are available in countries around the world, and our applications support our mission of delivering music to customers in new and exciting ways that enhance their day-to-day lives.