Careers

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research

AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for an Applied Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Applied Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases

The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians on a mission to develop a fault-tolerant quantum computer. As a Quantum Applied Scientist on our Device and Architecture Theory team, you will focus on the theory and modeling of our superconducting qubit processors. You will work closely with our measurement, design, and calibration teams to interpret experimental results, inform new processor designs, and optimize device performance. You will also conduct pathfinding research to guide the development of next-generation processors, focusing on device-level physics, novel qubits and gate schemes, and implementations of error correcting codes. We are looking for candidates with excellent problem-solving and communication skills, and with a genuine passion for applied, collaborative work. Key job responsibilities - Develop theoretical and numerical models of superconducting qubit processors, working across theory, measurement, design, and calibration teams to validate predictions and translate modeling insights into device improvements - Conduct pathfinding research on novel qubit designs, gate schemes, and processor architectures to push the performance of next-generation devices - Communicate scientific findings across the CQC, and, when appropriate, share results externally via conference presentations and publications in scientific journals - Stay abreast of new research developments in the field of quantum computation About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, 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 conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon 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. 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. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.

The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.

Are you passionate about solving large-scale optimization and machine learning problems that directly improve how Amazon operates? The FinAuto team is hiring a Research Scientist to support GREF, Amazon’s Global Real Estate and Facilities organization. GREF is responsible for managing Amazon’s workplace, facilities, transportation, and related operational services across a large and complex global footprint. This role offers a unique opportunity to work on science problems where Operations Research, forecasting, machine learning, and decision optimization come together. Example problem areas include optimizing employee cab routes, forecasting transportation demand, improving facility capacity planning, optimizing vendor and resource allocation, reducing operational cost, improving service reliability, and building scalable decision systems for real-world planning problems. Key job responsibilities As a Research Scientist, you will translate ambiguous business problems into structured mathematical and scientific solutions. You will work with business, product, engineering, and operations teams to define the problem, identify the right modeling approach, build scalable algorithms, and help productionize solutions that influence operational decisions. The ideal candidate will have strong depth in Operations Research, applied mathematics, optimization, statistics, and machine learning, along with the ability to operate independently in a fast-moving environment. This role is well suited for a scientist who wants to work on practical, high-impact problems with large-scale data, complex constraints, and measurable business outcomes.

The WW Operations IPAT team is revolutionizing Amazon’s financial forecasting through TrendCast, an innovative, automated, science-based top-down forecast modeling engine. As we expand our scope into Generative AI, we are building a sophisticated, LLM-powered Finance Knowledge Base to streamline decision-making. We are seeking a strong Data Scientist II to drive the technical strategy for these advanced analytical and AI-driven solutions. In this role, you will act as a technical lead, translating high-level business ambiguity into scalable, production-grade systems while influencing cross-functional roadmaps. Key job responsibilities • Own and solve difficult business problems where the solution approach is unclear, delivering high-quality artifacts that directly influence financial decisions for senior leadership • Apply a range of data science methodologies (statistical modeling, machine learning, time series analysis, econometrics) to solve complex forecasting challenges • Design and implement scalable, reliable approaches to extract insights from large, complex datasets across multiple domains • Develop metrics to quantify the benefits of solutions and measure project progress and success • Design and implement Retrieval-Augmented Generation (RAG) systems and LLM-based solutions to enhance financial knowledge retrieval and decision support • Proactively identify and solve challenges related to GenAI solutions including accuracy, latency, and context management • Partner with finance stakeholders, engineers, and other scientists to identify data requirements and deliver solutions that meet customer needs • Write clear, factually correct documents with substantial analytical components; explain technical concepts to non-technical audiences • Provide peer feedback on solutions and results; mentor and teach less experienced data scientists

Are you passionate about authorization, programming languages, applying formal verification, program analysis, constraint-solving, and/or theorem proving to real-world problems? Do you want to shape the future of an open-source authorization language that is becoming an industry standard? If so, then we have an exciting opportunity for you. Cedar is an open-source policy language and evaluation engine for authorization that is used across AWS services including Amazon Verified Permissions, AWS Systems Manager, and more. Cedar recently joined the Cloud Native Computing Foundation (CNCF) as a Sandbox project, and we are looking for an Applied Scientist to help advance Cedar's adoption, maturity, and community presence across the cloud-native ecosystem. In this role, you will drive the science and engineering behind Cedar's integration into cloud-native platforms such as Kubernetes, advance Cedar's formal verification and analysis capabilities, and serve as a technical leader and advocate within the CNCF community. You will interact with internal teams and external open-source communities to understand their authorization requirements, propose innovative solutions, create software prototypes, and productize prototypes into production systems. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. Key job responsibilities Technical Responsibilities - Drive the design and development of Cedar's integration into cloud-native authorization environments, including Kubernetes and other CNCF ecosystem projects. - Advance Cedar's formal verification, SMT-based analysis, and policy validation capabilities to raise the bar for authorization assurance. - Interact with various teams to develop an understanding of their security, authorization, and policy requirements. - Apply the acquired knowledge to build tools that find problems, or show the absence of security/safety problems, in authorization policies and systems. - Implement these tools through the use of SAT, SMT, and various concepts from programming languages, theorem proving, formal verification, and constraint solving. - Create software prototypes to verify and validate devised solutions; integrate prototypes into production systems using standard software development tools and methodologies. - Contribute to Cedar's open-source codebase as a maintainer, driving code quality, review standards, and technical direction. Leadership & Community Responsibilities - Represent Cedar and AWS at technical conferences, including CNCF events such as KubeCon, and advocate for Cedar adoption across the cloud-native community. - Can present and defend company-wide technical decisions to the internal technical community and represent the company effectively at technical conferences. - Functional thought leader, sought after for key tech decisions. Can successfully sell ideas to an executive-level decision maker. - Mentor and train the research scientist community on complex technical issues. - Collaborate with the open-source community to advance Cedar's CNCF project maturity (Sandbox → Incubation → Graduated). - Build and maintain relationships with cloud-native developers, contributors, and organizations to drive Cedar adoption and gather feedback. A day in the life You will be working on cutting-edge technology at the intersection of formal methods, automated reasoning, authorization, and cloud-native systems. You will collaborate with fellow applied scientists and engineers to solve challenging problems that provide value to customers by improving the security and usability of authorization. You will engage with the open-source community, contribute to Cedar's CNCF journey, and have an opportunity to publish your work and present at leading industry conferences. About the team The Cedar team builds and maintains Cedar, an open-source policy language and evaluation engine for authorization. Cedar is designed to be ergonomic, fast, and analyzable, backed by automated reasoning and formal verification. Cedar is used across multiple AWS services and has joined the CNCF as a Sandbox project, with the goal of becoming a Graduated project and an industry standard for authorization. The team works at the intersection of programming languages, formal methods, and cloud-native infrastructure.

Are you passionate about solving complex business problems at scale through Generative AI? Do you want to help build intelligent systems that reason, act, and learn from minimal supervision? If so, we have an exciting opportunity for you on Amazon's Trustworthy Shopping Experience (TSE) team. At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy. We do this in close partnership with our selling partners, empowering them with best-in-class tools and expertise to offer a high-quality, compliant selection that customers trust. As an Applied Scientist I, you will bring subject matter expertise in at least one relevant discipline (e.g., NLP, computer vision, representation learning, agentic architecture) to contribute to next-generation agentic AI solutions that automate complex manual investigation processes at Amazon scale. Working alongside senior scientists, you will map business goals—such as reducing cost-of-serving while maintaining trust and safety standards—to well-defined scientific problems and metrics. You will invent, refine, and experiment with solutions spanning agentic reasoning, self-supervised representation learning, few-shot adaptation, multimodal understanding, and model compression. With guidance from senior scientists, you will stay current on research trends and benchmark your results against the state of the art. You will help design and execute experiments to identify optimal solutions, initiating the development and implementation of small components with team guidance. You will write secure, stable, testable, and well-documented production code at the level of an SDE I, rigorously evaluating models and quantifying performance. You will handle data in accordance with Amazon policies, troubleshoot issues to root cause, and ensure your work does not put the company at risk. Your scope of influence will typically be at the self-level, with the possibility of mentoring interns. You will participate in team design and prioritization discussions, learn the business context behind TSE's products, and escalate problems with proposed solutions. You will publish internal technical reports and may contribute to peer-reviewed publications and external review activities when aligned with business needs. This role offers a unique opportunity to contribute to end-to-end AI development—from research through production—with your contributions serving hundreds of millions of customers within months, not years. Key job responsibilities •Contribute to the design and development of agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, including feedback and memory mechanisms, leveraging reinforcement learning techniques for agent decision-making and policy optimization, with input and guidance from senior scientists • Design and build expertise agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence with capabilities to handle feedback with long term as well as short term memory mechanisms. • Productionize large scale models built on top of SFT (Supervised Finetuning) and RFT (Reinforced fine tuning) approaches (GRPO with RLVR, Process/Outcome Reward Models), few shot approaches (Contrastive, Prototypical) based on multimodal datasets • Enhance on existing Automatic prompt optimization techniques (GEPA & beyond) towards agentic optimization given the ground truth datasets to improve agentic planning. • Build novel production ready Finetuned transformer architectures (using LORA/Q-LORA/LLM-JEPA etc) and conventional supervised & unsupervised ML solutions to aid the multiple potential automation requirements • Identify customer and business problems at project level; invent or extend state-of-the-art approaches for complex LLM workflows involving unstructured text, documents, images, and relational data •Contribute to building production-ready deep learning and conventional ML solutions, including multimodal fusion and cross-modal alignment techniques that seamlessly connect visual, textual, and relational understanding, to support automation requirements within your team's scope •May co-author research papers for peer-reviewed internal and/or external venues, including contributions in areas such as multimodal representation learning and vision-language modeling, and contribute to the wider scientific community by reviewing research submissions, when aligned with business needs •Prototype rapidly, iterate based on feedback, and deliver small components at SDE I level—including multimodal data pipelines and inference modules—that integrate into production-scale systems •Write secure, stable, testable, maintainable, and well-documented code, balancing model capability, deployment cost, and resource usage across multimodal architectures while understanding state-of-the-art data structures, algorithms, and performance tradeoffs •Rigorously test code and evaluate models across individual and combined modalities, quantifying their performance; troubleshoot issues, research root causes, and thoroughly resolve defects, leaving systems more maintainable •Participate in team design, scoping, and prioritization discussions through clear verbal and written communication; seek to learn the business context, science, and engineering behind your team's products, including how multimodal signals contribute to trust and safety decisions •Participate in engineering best practices with peer reviews; clearly document approaches and communicate design decisions; publish internal technical reports to institutionalize scientific learning •Help train and mentor scientist interns; identify and escalate problems with proposed solutions, taking ownership or ensuring clear hand-off to the right owner About the team Trustworthy Shopping Experience Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.

We build AI-powered tooling that enables security operations to scale with AWS's growth. Our portfolio includes generative AI incident response assistants, natural language-driven response, detection enrichment pipelines, and security data analytics platforms. Security analysts depend on these systems around the clock. We are hiring a Senior Applied Scientist to own the science strategy for our AI security response platform. You will define and execute the machine learning and AI roadmap across our service portfolio, from large language model-powered incident triage to anomaly detection in security telemetry. You will extend and invent techniques at the product level, partnering with software and security engineers to bring models from research into production systems that operate 24/7/365. You will be the scientific authority on the team, expected to teach, mentor, and set the technical bar for how we apply AI to security operations problems. This role requires deep expertise in natural language processing, generative AI, or a closely related discipline, combined with a demonstrated ability to translate scientific methods into production systems that solve real business problems. You will operate in high-ambiguity, high-consequence domains where your scientific judgment directly affects security outcomes for AWS. Key job responsibilities - Define and own the science strategy for the team's AI-powered security automation portfolio, including model selection, evaluation methodology, and research direction. - Design and implement LLM-powered systems for security incident triage, including retrieval-augmented generation, prompt engineering, and fine-tuning approaches that improve recommendation accuracy and reduce analyst toil. - Build anomaly detection and classification models across security telemetry data sources to surface threats, reduce false positives, and prioritize analyst attention. - Partner with software engineers to move models from experimentation to production. Define system-level technical requirements, guide adaptation to meet production constraints, and own model performance in deployment. - Develop evaluation frameworks and metrics that measure model effectiveness against security outcomes, not just standard ML benchmarks. - Mentor software and security engineers on ML best practices and raise the science bar across the team through design reviews, code reviews, and knowledge sharing. A day in the life You start by reviewing model performance dashboards for overnight incident triage recommendations, investigating a drift in precision for a specific detection category. Mid-morning, you lead a design review for a retrieval-augmented generation pipeline that will surface relevant runbooks during security incidents. After lunch, you pair with a security analyst to label edge cases that your current model misclassifies, turning operational feedback into training signal. You close the day writing an experiment plan to evaluate a new embedding approach for security log similarity, then sync with your manager on the quarterly science roadmap. About the team This team operates within AWS Security in a 24/7/365 organization that protects AWS's global cloud infrastructure. The team builds AI-powered security automation, data analytics platforms, and incident response tooling that security analysts depend on around the clock. We work at the intersection of machine learning, generative AI, and security operations. Our mission: give every security analyst the intelligent tooling they need to stay ahead of threats at AWS scale. 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.