Careers

The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers on a mission to develop a fault-tolerant quantum computer. Throughout your internship journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of Quantum Computing and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Quantum Research Science and Applied Science Internships in San Francisco, CA; Santa Clara, CA; Pasadena, CA; and Boston, MA. We are particularly interested in candidates with expertise in any of the following areas: superconducting qubits, cavity/circuit QED, quantum optics, open quantum systems, superconductivity, electromagnetic simulations of superconducting circuits, microwave engineering, benchmarking, quantum error correction, fabrication, etc. Key job responsibilities In this role, you will work alongside global experts to develop and implement novel, scalable solutions that advance the state-of-the-art in the areas of quantum computing. You will tackle challenging, groundbreaking research problems, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex problems and to communicate research findings clearly. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. Critically review and validate outputs from AI tools and automated systems. About the team Diverse Experiences AWS 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 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 Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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 in the cloud.

Do you have a passion for data? Are you matriculating in a Master’s or PhD program? Amazon is looking for driven data science students with strong modeling skills who are comfortable owning and executing data. To be successful in this internship, you will need the ability to develop, automate, and run analytical models for our systems. During this internship, you will build tools and support structures needed to analyze and dive deep into data to resolve systems errors and changes. You will have the ability to present your findings to our business partners and help drive improvements. Previous applicants demonstrated the aptitude to manage medium-scale modeling projects, identified requirements, and built methodology/tools that were statistically grounded. For more information on the Amazon Science community please visit https://www.amazon.science

At Amazon, Security is a top priority. Are you interested in shaping the future of security for AWS, our customers, and the broader open source community? Do you want to be part of building something with massive impact? Our AWS Security Customer Outcomes Team is looking for a strong applied scientist as we help improve the security of open source software and the safety of the software supply chain generally. We're looking for an ambitious team member who will bring added scientific rigor and innovation to how our customer's experience security with and through open source software. Our systems operate on a global scale across a diverse ecosystem of field teams, security requirements, and tooling which requires high velocity security innovation that scales. We are seeking an Applied Scientist to help define, design, build, and operate AI-powered security solutions across the full lifecycle of open source software usage. You will work alongside a team to build intelligent automation that augments manual identification, analysis, and remediation workflows, enforces security controls for agents and human experts, and provides data-driven insights to AWS leadership. Key job responsibilities - Architect and build AI-powered security applications and tooling — Design and implement LLM-based systems (leveraging Bedrock, SageMaker, and state of the art patterns) for intelligent code scanning, automated solution development/deployment, and remediation alternatives. - Transform manual processes – Reimagine and deliver change from low throughput, human-dependent processes into high volume, machine speed pipelines without sacrificing key quality metrics. - Collaborate with and influence other leaders – The open source ecosystem has many stakeholders, and change requires listening and supporting but also influencing and leading through action. - Drive proactive security automation — Build systems that identify and remediate security issues in deliverables (code, threat models, content) without requiring explicit builder consent at every step, moving from reactive reviews to proactive prevention - Own operational excellence — Define and maintain SLAs, monitoring, alerting, runbooks, and incident response for services you own; participate in on-call rotation to support 24/7 availability - Lead technical design — Produce design documents, conduct trade-off analysis, and drive alignment across organizations. - Design and conduct scientific research using machine learning and deep learning techniques to address complex, ambiguous problems, working backwards from customer needs to invent new approaches or extend existing ones. - Build, train, and evaluate production-quality models using frameworks such as PyTorch or TensorFlow, applying rigorous experimentation, feature engineering, and hyperparameter optimization to improve accuracy and performance. - Write clean, maintainable code with optimal data structures and algorithms, ensuring your components integrate directly into production systems and meet high standards for operational reliability and resource efficiency. - Collaborate with engineering and product partners to translate scientific insights into scalable solutions, clearly communicating design decisions and trade-offs to ensure long-term maintainability. - Contribute to the scientific community by authoring or co-authoring peer-reviewed publications, mentoring less experienced scientists, and participating in technical reviews across teams. A day in the life You might start your morning reviewing experiment results and refining a model architecture before syncing with engineering partners on an upcoming production integration. After lunch, you could dive into a research paper relevant to a challenge your team is tackling, then prototype a new approach and set up evaluation pipelines. Later, you may join a design review to share your findings or pair with a teammate to debug a tricky data pipeline issue. Throughout, you balance hands-on scientific exploration with collaborative problem-solving. About the team 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. 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.

The NASC & TOM Science team owns Operations Research, Machine Learning, and AI projects across the North America Sort Center (NASC) and Transportation Operations Management (TOM) planning and operations organizations. We turn complex network, labor, and capacity problems into deployed models that drive multi-million-dollar planning decisions every day. As a Data Scientist II, you will own the end-to-end Machine learning Operation cycle: Design, build, and ship machine learning and/or optimization models that directly shape Amazon's middle miles planning decisions. You will own end-to-end delivery — from problem framing with business partners, through modeling and validation, to deployment in internal model hosting platform and integration with downstream planning tools. You will work on problems such as: Long- and short-horizon forecasting Network and capacity optimization GenAI / agentic systems Defect prevention and adaptive planning You will partner closely with Engineering, Product, Engineering, and stakeholders to translate ambiguous operational pain points into measurable model outcomes. Key job responsibilities - Design and implement complex ML and optimization solutions (forecasting, MIP/LP, simulation, Deep learning / foundation model) - Drive end-to-end delivery of scalable models. From data exploration and feature engineering through training, evaluation, deployment, and post-launch monitoring; - Develop new modeling patterns and analytical frameworks for forecasting (multivariate, hierarchical, causal-DAG, model-chaining) and optimization; - Build robust model validation, backtesting, and monitoring pipelines; identify and eliminate sources of leakage, bias, and silent failure; - Define and own model performance metrics (e.g., WAPE) tied to business outcomes; - Partner with Data Engineering and Software Development to productionize models and define I/O contracts, packaging, and model CI/CD; - Excellent communication to present findings, tradeoffs, and recommendations clearly to stakeholders and senior leadership.

As an Applied Scientist on the OMHS Software Controls and Science team, you will build and deploy first-of-its-kind physical AI capabilities within Amazon Manufacturing Services. You will turn applied research into functional prototypes and production systems that operate on real parts under real shop floor conditions. This role combines applied ML, 3D perception, and geometric reasoning with a strong manufacturing-outcome focus. It will be your job to frame ambiguous fabrication problems as tractable scientific problems, and to deploy perception and part-recognition systems on robotic welding cells that match physical parts to their CAD models an determine orientation, tooling, and fixturing without manual programming. You will own the full loop from prototype to measurable result: instrumenting robotic cells with sensors, collecting real-world manufacturing data, training and iterating models against accuracy and cycle-time targets, and integrating your work into robot motion planning and production software pipelines. Your near-term mission is to help enable a lossless digital thread from engineering specification to autonomous robotic execution, starting with welding, in a high-mix environment scaling toward high-volume production. Key job responsibilities • Deploy 3D perception and part-recognition systems on robotic welding cells that match physical parts to their CAD models and determine part orientation, tooling, and fixturing without manual programming. • Build software pipelines that connect CAD and engineering-drawing interpretation to robot motion planning and execution in a production manufacturing environment. • Frame ambiguous fabrication problems as tractable scientific problems, and prototype solutions end to end — from sensor setup and data collection to model training, deployment, and performance evaluation. • Evaluate, recommend, and integrate commercial robotic platforms, 3D scanning systems, and sensor hardware to support physical AI development on the shop floor. • Instrument robotic cells, collect real-world manufacturing data, and iterate on model performance against measurable accuracy and cycle-time targets. • Collaborate across automation engineering and software engineering teams to integrate your solutions into the shop floor deployment architecture. 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.

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

The Sponsored Products and Brands team at Amazon Ads is re-imagining advertising through generative AI technologies, transforming how millions of customers discover products and engage with brands across Amazon.com and beyond. We are 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 - Design and implement machine learning models and algorithms that power advertiser-facing AI experiences, ensuring scientific rigor from research through production deployment. - Conduct applied research to extend or invent new approaches for complex advertising problems where no textbook solutions exist, working backwards from advertiser needs. - Collaborate with engineering, product, and science teams to integrate your solutions directly into large-scale production systems serving millions of advertisers. - Analyze experimental results and system performance to identify improvement opportunities, making informed tradeoffs between model complexity, latency, and business impact. - Mentor other scientists, provide peer feedback on research procedures and results, and contribute to the team's scientific roadmap and technical direction. A day in the life You start your morning reviewing experiment results from a model you recently launched, checking metrics and identifying areas for iteration. By mid-morning you're whiteboarding a new approach with fellow scientists and engineers, debating tradeoffs between model architectures. After lunch you write and test code for a prototype, then join a design review where you share your findings and gather feedback. You wrap up by drafting a short document outlining next steps for a research proposal you plan to share with the broader team. About the team Our team within Amazon Ads builds AI-powered systems that help advertisers succeed at scale. We develop personalized, context-aware guidance tools grounded in large language models and advanced reasoning frameworks, connecting advertisers with actionable insights across multiple surfaces. Our mission is to make advertising simpler and more effective for businesses of all sizes. We value scientific creativity, collaborative problem-solving, and shipping real solutions that reach millions of advertisers worldwide.

What will customers want from Amazon Devices one, two, or three years from now? As a Data Scientist II on our Devices forecasting team, you will answer that question by building econometric and machine learning models that project long-term demand, assess the incrementality of new products, and quantify willingness to pay for specific features. Your analysis will directly shape portfolio decisions, helping product managers decide what to build next. This is a team that is investing in AI to accelerate how science informs business strategy, making now a particularly exciting time to join. Key job responsibilities - Build and validate econometric and machine learning models that generate long-term demand forecasts for Amazon Devices, selecting the right methodology based on data characteristics and business context. - Assess the incrementality of new products and quantify willingness to pay for product features, translating model outputs into clear narratives that help product managers adjust their portfolio strategy. - Collaborate with product managers, engineers, and business stakeholders to scope analytical projects, define metrics, and identify the data requirements needed to answer ambiguous forecasting questions. - Communicate findings to technical and non-technical audiences through clear documentation, effective visualizations, and well-structured presentations that drive informed decisions. - Mentor less experienced data scientists through code reviews, knowledge sharing, and active participation in scientific discussions and team planning. A day in the life You might spend your morning refining a demand forecast model, testing how a new product feature variable improves prediction accuracy. After lunch, you could be walking product managers through your incrementality analysis and aligning on what the numbers mean for their roadmap. Later, you might review a teammate's willingness-to-pay study or experiment with an AI-based approach to accelerate your modeling pipeline. Your work moves between deep independent analysis and collaborative sessions where you translate complex results into actionable recommendations. About the team Our team owns long-term forecasting and product analytics for Amazon Devices. We build the science that tells the story of where customer demand is headed and what drives it. You will work alongside scientists, engineers, and product managers who value rigorous analysis and practical impact. We are currently expanding our use of AI to accelerate how we deliver insights, and we are looking for people who are curious, collaborative, and ready to help shape that direction.

Do you want to lead the Ads industry and redefine how we measure the effectiveness of Amazon Ads business? Are you passionate about causal inference, Deep Learning & AI, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be a science leader within our Advertising Incrementality Measurement science team! Our work builds the foundations for providing customer-facing advertising measurement tools, furthering internal research & development, and building out Amazon's advertising measurement offerings. Incrementality is a lynchpin for the next generation of Amazon Advertising measurement solutions, and this role will play a key role in the release and expansion of these offerings. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and/or econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers to define key business questions and work with the engineering team to bring our solutions into production. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon advertising, and also allow you to become part of our large science community. Key job responsibilities • Apply expertise in ML/DL, AI, and causal modeling to develop new models that describe how advertising impacts customers’ actions • Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions • Improve upon and simplify our existing solutions and frameworks • Review and audit modeling processes and results for other scientists, both junior and senior • Work with leadership to align our scientific developments with the business strategy • Identify new opportunities that are suggested by the data insights • Bring a department-wide perspective into decision making • Develop and document scientific research to be shared with the greater science community at Amazon About the team AIM is a cross disciplinary team of engineers, product managers, economists, data scientists, and applied scientists with a charter to build scientifically-rigorous causal inference methodologies at scale. Our job is to help customers cut through the noise of the modern advertising landscape and understand what actions, behaviors, and strategies actually have a real, measurable impact on key outcomes. The data we produce becomes the effective ground truth for advertisers and partners making decisions affecting millions in advertising spend.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for International Emerging Stores (IES). Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the International Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions across International Emerging Store (India, MENA, Far-East, LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide