Careers

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.

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 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

Amazon’s Last Mile Team is looking for a passionate individual with strong machine learning and GenAI engineering skills to join its Last Mile Science team in the endeavor of designing and improving the most complex planning of delivery network in the world. Last Mile builds global solutions that enable Amazon to attract an elastic supply of drivers, companies, and assets needed to deliver Amazon's and other shippers' volumes at the lowest cost and with the best customer delivery experience. Last Mile Science team owns the core decision models in the space of jurisdiction planning, delivery channel and modes network design, capacity planning for on the road and at delivery stations, routing inputs estimation and optimization. Our research has direct impact on customer experience, driver and station associate experience, Delivery Service Partner (DSP)’s success and the sustainable growth of Amazon. Optimizing the last mile delivery requires deep understanding of transportation, supply chain management, pricing strategies and forecasting. Only through innovative and strategic thinking, we will make the right capital investments in technology, assets and infrastructures that allows for long-term success. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. Key job responsibilities Candidates will be responsible for developing solutions to better manage and optimize delivery capacity in the last mile network. The successful candidate should have solid research experience in one or more technical areas of Machine Learning, Computer Vision, or GenAI. These positions will focus on identifying and analyzing opportunities to improve existing algorithms and also on optimizing the system policies across the management of external delivery service providers and internal planning strategies. They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs.

Esta é uma posição de colaborador individual, com base em nosso escritório de São Paulo. Procuramos uma pessoa dinâmica, analítica, inovadora, orientada para a prática e com foco inabalável no cliente. Na Amazon, nosso objetivo é exceder as expectativas dos clientes, garantindo que seus pedidos sejam entregues com máxima rapidez, precisão e eficiência de custo. A determinação da rota de cada pacote é realizada por sistemas complexos, que precisam acompanhar o crescimento acelerado e a complexidade da malha logística no Brasil. Diante desse cenário, a equipe de Otimização de Supply Chain está à procura de um cientista de dados experiente, capaz de desenvolver modelos, ferramentas e processos para garantir confiabilidade, agilidade, eficiência de custos e a melhor utilização dos ativos. O candidato ideal terá sólidas habilidades quantitativas e experiência com conjuntos de dados complexos, sendo capaz de identificar tendências, inovar processos e tomar decisões baseadas em dados, considerando a cadeia de suprimentos de ponta a ponta. Key job responsibilities * Executar projetos de melhoria contínua na malha logística, aproveitando boas práticas de outros países e/ou desenvolvendo novos modelos. * Desenvolver modelos de otimização e cenários para planejamentos logísticos. * Criar modelos de otimização voltados para a execução de eventos e períodos de alta demanda. Automatizar processos manuais para melhorar a produtividade da equipe. * Auditar operações, configurações sistêmicas e processos que possam impactar custos, produtividade e velocidade de entregas. * Realizar benchmarks com outros países para identificar melhores práticas e processos avançados, conectando-os às operações no Brasil. About the team Nosso time é composto por engenheiros de dados, gerentes de projetos e cientistas de dados, todos dedicados a criar soluções escaláveis e inovadoras que suportem e otimizem as operações logísticas da Amazon no Brasil. Nossa missão é garantir a eficiência de todas as etapas da cadeia de suprimentos, desde a primeira até a última milha, ajudando a Amazon a entregar resultados com agilidade, precisão e a um custo competitivo, especialmente em um ambiente de rápido crescimento e complexidade.

Are you interested in working with top talents in Optimization, Operations Research and Supply Chain to help Amazon to efficiently match our Devices with worldwide customers? We have challenging problems and need your innovative solutions to make tremendous financial impacts! The Amazon Demand Science Optimization organization is looking for an Applied Scientist with background in Operations Research, Optimization, Supply Chain, Simulation, and Gen AI to support science efforts to integrate across inventory management functionalities. Our team is responsible for science models (both deterministic and stochastic) that power world-wide inventory allocation, promotion optimization for Amazon Devices business that includes Echo, Kindle, Fire Tablets, Amazon TVs, Amazon Fire TV sticks, Ring, and other smart home devices. We formulate and solve challenging large-scale financially-based optimization problems which ingest demand forecasts and produce optimal price promotion strategies, procurement, production, distribution, and inventory management plans. In addition, we also work closely with the demand forecasting, material procurement, production planning, finance, and logistics teams to co-optimize the inventory management and supply chain for Amazon Devices given operational constraints. Key job responsibilities The successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail, and an ability to work in a fast-paced and ever-changing environment and a desire to help shape the overall business. Responsibilities include: - Design and develop advanced mathematical, simulation, and optimization models and apply them to define strategic and tactical needs and drive appropriate business and technical solutions in the areas of inventory management and distribution, network flow, supply chain optimization, and demand planning - Apply mathematical optimization techniques (linear, quadratic, SOCP, robust, stochastic, dynamic, mixed-integer programming, network flows, nonlinear, nonconvex programming) and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software - Research, prototype and experiment with these models by using modeling languages such as Python; participate in the production level deployment - Create, enhance, and maintain technical documentation, and present to other Scientists, Product, and Engineering teams - Lead project plans from a scientific perspective by managing product features, technical risks, milestones and launch plans - Influence the organization's long-term roadmap and resourcing, onboard new technologies onto Science team's toolbox, mentor other Scientists About the team Amazon Science https://www.linkedin.com/showcase/amazonscience/posts/?feedView=all

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Data Scientist III Job Location: San Diego, California Job Number: AMZ9803634 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range $159,200/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000

Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation

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 Agentic Automated Reasoning Group is pioneering the next generation of neuro-symbolic tools—fusing breakthroughs in artificial intelligence with the scale of the cloud and our deep expertise in automated reasoning. If you're driven to push the boundaries of what's possible at the intersection of learning and logic, join us and help shape this transformational initiative. The Automated Reasoning checks team is looking for an Applied Scientist with experience in building scalable formal reasoning solutions that delight customers. You will be part of a world-class team building the next generation of tools and services by combining Automated Reasoning, GenAI, and Agentic AI at cloud computing scale. You will apply your knowledge to propose solutions, create software prototypes, and move prototypes into production systems using modern software development tools and methodologies. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. You will use your strong verbal and written communication skills and own the delivery of high-quality results in a fast-paced environment. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. See https://aws.amazon.com/security/provable-security/ As an Applied Scientist in the Agentic Automated Reasoning Group, you will play a pivotal role in shaping product features from beginning to end. You will: * Define and implement new automated reasoning features that employ scalable and efficient approaches to solve complex problems using neural learning and symbolic/formal reasoning * Apply software engineering best practices to ensure a high standard of quality for all team deliverables * Work in an agile, startup-like development environment * Deliver high-quality scientific artifacts * Work with the team to help drive business decisions Key job responsibilities * Design and implement scalable, production-grade neuro-symbolic systems that integrate formal reasoning with GenAI to deliver reliable, verifiable outcomes for AWS customers. * Design and run reinforcement learning pipelines (GRPO, PPO, DPO) to optimize language models for formal reasoning and autoformalization tasks. * Design and run experiments to measure model quality, latency, and cost tradeoffs across model sizes and training strategies. * Collaborate cross-functionally with product, engineering, and science teams as well as external customers to deeply understand pain points, gather requirements, and translate them into neuro-symbolic features that solve real-world problems. * Enhance and extend the capabilities of formal reasoning systems to meet the demands of GenAI and agentic applications — including areas such as hallucination detection, policy verification, and automated guardrails. * Own the end-to-end science lifecycle — from research and experimentation through production deployment — defining metrics to measure system performance and the real-world impact of neuro-symbolic solutions. * Advance the state of the art through publications at top-tier venues, patents, or open-source contributions, strengthening Amazon's position as a leader in automated reasoning and neuro-symbolic AI. A day in the life As an Applied Scientist on the Agentic Automated Reasoning team, you'll design and build neuro-symbolic systems that mathematically verify AI-generated policy content. Day to day, you'll run experiments and invent features to improve Automated Reasoning checks in Amazon Bedrock Guardrails, collaborate with engineering and product teams to ship features into production, and partner with other AWS agentic AI teams to integrate neuro-symbolic reasoning into workflows. You'll practice customer obsessed science for our customers in regulated industries to translate real-world policy challenges into research priorities, while publishing at top-tier venues. About the team You will be working with a team of formal methods and machine learning specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of new features for Automated Reasoning checks that delight our customers. Why AWS? 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 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.