Careers

AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow. Are you a customer-obsessed builder passionate about helping enterprise customers achieve their full potential with Generative AI? Do you have deep data science expertise and the technical pre-sales acumen to help customers evaluate, design, and deploy GenAI and ML solutions on AWS? Do you enjoy building impactful AI agents and agentic applications? Join the GenAI/ML Specialist organization as a Senior Generative AI Data Scientist — a senior individual contributor role requiring deep data science expertise, hands-on ML engineering skills, and executive-level customer engagement. The AGS Specialist organization is part of the customer-facing NAMER sales organization, and is responsible for driving revenue and accelerating adoption of cloud and partner services across diverse customer segments. We work backwards from our customers' most complex and business-critical challenges to develop and execute go-to-market plans that transform ideas into scalable, high-impact businesses. AGS NAMER teams include sales specialists and technical solution architects. As part of the team, you will contribute across the full lifecycle of AWS customer initiatives—from shaping new service and solution concepts to accelerating adoption of established offerings. We pride ourselves on thinking big, delivering exceptional customer outcomes, and collaborating seamlessly across AWS as #OneTeam. Role Description In this role, you will be the Subject Matter Expert (SME) for helping NAMER Enterprise customers design and implement Generative AI solutions that leverage Amazon Bedrock. You will translate customer business challenges into data science-driven solutions using AWS. You will define, design, and deploy machine learning models, agentic workflows, and GenAI applications that accelerate adoption of AWS AI/ML services. You will engage with senior engineers, data scientists, product leaders, and executives at strategic enterprise customers to influence technical decisions and provide structured feedback to AWS product teams. You will interact with customers directly to understand their business problems, help and aid them in implementation of generative AI solutions, deliver briefings and deep dive sessions, and guide customers on adoption patterns and best practices for generative AI. You will build prototypes, proof-of-concepts, and explore novel solutions leveraging Amazon Bedrock, Amazon AgentCore, Strands Agents, and open source frameworks such as LangChain, LangGraph, and CrewAI. This position will focus on agentic workflows with Amazon Bedrock, including Strands Agents, Bedrock Agents, and open source agentic frameworks. You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, and fine-tuning techniques. You should be proficient with design, deployment, and evaluation of LLM-powered agents, tools, and orchestration approaches. You will interface with customer data science teams, ML engineering leadership, and C-suite executives to advise on the latest techniques, model architectures, and emerging research. This includes staying current with state-of-the-art approaches from recent publications and research papers — such as advances in reasoning models, multi-agent systems, retrieval-augmented generation, reinforcement learning from human feedback (RLHF), and novel fine-tuning methods — and translating those findings into practical, production-ready solutions for enterprise customers. You will lead technical deep dives and whiteboard sessions with customer chief data scientists and VPs of AI/ML, bridging the gap between research and real-world implementation on AWS. You should understand the security and compliance requirements for ML/GenAI implementations. You should have experience architecting end-to-end ML/GenAI agentic applications for customers using AWS services and the Well-Architected Framework. As the ideal candidate, you bring a deep data science background and the business acumen required to lead complex engagements with large enterprises. You have hands-on expertise in statistical modeling, traditional ML, and current areas such as LLMs, RAG, fine-tuning, AI system evaluation, prompt engineering, agents, and AIOps. You are able to credibly advise senior technical and executive stakeholders on architectural trade-offs, best practices, and risk mitigation. Key job responsibilities - Working with NAMER Enterprise customers' development and data science teams to deeply understand their business and technical needs. Design and implement solutions that make the best use of the AWS cloud platform and AWS AI/ML services including SageMaker, Amazon Bedrock, Amazon AgentCore, and other AI/ML services. - Customer Advisor — Implement and deploy state-of-the-art machine learning and Generative AI solutions. Build prototypes, PoCs, and explore new solutions. Interact closely with enterprise customers to accelerate their AI/ML adoption. - Partner with Data Scientists, SAs, Sales, Business Development, and the AI/ML Service teams to accelerate customer adoption and revenue attainment in NAMER for Amazon Bedrock, SageMaker, and related services that support GenAI use cases. - Thought Leadership – Evangelize AWS GenAI services and share best practices through forums such as AWS blogs, whitepapers, reference architectures, and public-speaking events such as AWS Summit, AWS re:Invent, etc. - Act as a technical liaison between customers and the Amazon Bedrock or other service teams to provide customer-driven product improvement feedback. - Develop and support an AWS internal community of GenAI-related subject matter experts in the AMERICAS. Create field enablement materials for the broader technical population, to help them understand how to integrate AWS GenAI solutions into customer architectures. A day in the life Your day will be dynamic and impactful. You'll engage with enterprise business leaders, dive deep into ML architectures and data science pipelines, and craft transformative AI strategies. You'll collaborate across teams, translating complex statistical and AI concepts into clear, actionable solutions that drive meaningful business outcomes for NAMER Enterprise customers. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.

Are you passionate about data, enjoy solving complex analytical problems, leveraging industry leading agentic AI technologies to derive insight at scale - all in a challenging, fast-paced environment? We are seeking an Applied Scientist to accelerate the growth of Amazon Internal Audit’s Data Science & Risk Intelligence initiatives. The team builds ML and AI solutions that expand self-service data utilization by audit teams, utilizing the right methods to derive deeper patterns, and surface insights to gain holistic perspectives while amplifying potential risk mitigation. Key job responsibilities - Partner with audit teams, product managers, engineers, and scientists to define and deliver machine learning and generative AI products that carry significant ambiguity, scale, and complexity, owning problems end-to-end, from framing through measurable impact. - Design, build, and own agentic AI systems, including multi-agent workflows, retrieval-augmented generation, and tool-using agents, that automate and augment audit work, and set the standard for how the team evaluates them through rigorous LLM-as-judge and human-aligned evaluation. - Apply statistical analysis and classical machine learning using SQL and scripting languages like Python/R over large datasets to develop insights and recommendations that strengthen internal audit. - Architect secure, scalable solutions on AWS machine learning and generative AI services (e.g., Bedrock, AgentCore, SageMaker), owning the full lifecycle from design through production deployment, monitoring, and iterative improvement. - Own and evolve the team's production and experimentation infrastructure, including deployment pipelines, observability and tracing, and evaluation harnesses. - Drive applied research by identifying and pursuing emerging techniques, and disseminate findings through internal and external publications, talks, and journal clubs. - Raise the technical bar across the team, including mentor junior scientists and engineers, review designs and code, and help shape the product and technical roadmap. A day in the life As an Applied Scientist, you will own ambiguous, high-impact problems and help shape the technical roadmap that connects risk to Amazon. You will drive AI products that make audit work more effective and efficient, increasingly centered on LLM and agentic systems. You set technical direction across the full arc of applied science. That means framing problems, making architecture decisions, defining how the team evaluates quality, and delivering solutions in production. The ideal candidate pairs deep machine learning expertise with a builder's instinct for production architecture. They thrive on ambiguity, mentor others, and follow a fast-moving research frontier. About the team Internal Audit’s mission is to help our businesses improve controllership, operational efficiency, and customer experience.

Amazon Robotics is seeking an exceptional Applied Scientist to join our Foundation Models and Agentic AI team. This role presents an opportunity to shape the future of robotics through innovative applications of large vision-language-action (VLA) models, reasoning and task planning models, and reinforcement learning. At Amazon Robotics, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, depth, force, tactile, linguistic) - Accelerate policy skill acquisition through imitation learning and reinforcement learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities As an Applied Scientist in the Applied AI and Robotics Foundations Model team, you will: - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.

Amazon Leo is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be 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. We are looking for an experienced Data Scientist to help architect state-of-the-art test infrastructure and lead the development of data models and analysis tools to represent the ground truth about satellite test results in order to facilitate important business decisions. Our team is responsible for core infrastructure and tools that will serve as the backbone of automated satellite testing operations to enable rapid scaling of manufacturing processes. Key job responsibilities * Work with engineering, software and manufacturing teams to understand drivers, impacts, and key influences on satellite performance * Lead the design, build and implementation of production models and make decisions in real time for satellite test results * Drive actions at scale to optimize test methodology and drive increases to satellite reliability * Analysis and modeling of satellite telemetry from test results in lab and on-orbit * Develop models and data pipelines for satellite telemetry * Create and manage datasets for continued pre-training and supervised fine-tuning of LLMs * Develop scalable visualizations for analysis of satellite performance A day in the life As Amazon Leo Data Scientist you will own the architecture definition and development of data analysis tools to to aid engineering and production teams in deciding flight-worthiness of each Amazon Leo satellite and historical traceability tools to enable simplified discovery and interpretation of past test data. You will work with multiple engineering, software and manufacturing teams across ground and space systems, to specify requirements, define data collection, interpretation strategies, data pipelines and implement data analysis and reporting tools for Integrated Vehicle tests. Your focus will be in optimizing the analysis of test results to enable Amazon Leo production plans. About the team The Automated Vehicle Testing Team is a mix of scientists and software engineers responsible for data infrastructure, tools, and research that serve as the backbone of automated satellite testing operations to enable rapid scaling of manufacturing processes.

Amazon's Worldwide Grocery Stores (WWGS), Data & Science team is seeking an Applied Scientist to join our Sales & Operations Planning (S&OP) and Supply Chain Science team. In this role, you will help build forecasting models that drive labor planning across the Amazon Grocery Network, where forecast misses can lead directly to staffing inefficiencies, higher costs, and degraded customer experience. You will contribute to the development and deployment of demand and labor forecasting models using Time-series, Bayesian and Structural methods, and Machine Learning. Senior scientists on the team will partner with you to scope problems and review designs, giving you room to build depth in forecasting science and production ML. You will also work directly with engineering partners, product owners, and business stakeholders, so you will see how your models change the decisions they make. Forecasts directly inform downstream labor and capacity decisions, so understanding how errors affect stakeholders is as important as improving accuracy. You will participate in design and roadmap discussions, communicate clearly with technical and non-technical partners, and develop judgment about the trade-offs in the systems you contribute to. We are investing in Generative AI to advance forecasting workflows, moving from human-in-the-loop to AI-in-the-loop decision support. Opportunities include automating forecast overrides for known events, identifying persistent bias, and augmenting planner and scientist judgment with agentic tools. Key job responsibilities - Develop, evaluate, and deploy components of our demand and labor forecasting models, including statistical time-series, Bayesian, and machine-learning models with distributional objectives, with input and guidance from senior scientists. - Translate business problems into well-defined scientific solutions with clear objectives, constraints, and success metrics, partnering with senior scientists on the more ambiguous ones. - Analyze forecast performance and downstream impact on labor planning and capacity decisions; develop metrics that reflect business outcomes, not only forecast accuracy. - Prototype and evaluate Generative AI approaches in our forecasting workflows and help productionize the ones that succeed. - Partner with engineering teams to produce models, contribute to data pipelines, and build scalable, maintainable forecasting systems. - Monitor deployed models, investigate performance issues, and continuously improve model quality and calibration. - Communicate technical concepts and recommendations clearly through documentation, presentations, and design reviews with scientists, engineers, product managers, and business leaders. - Contribute to the internal scientific community through knowledge sharing and, where appropriate, research publications.

Amazon's Compliance and Safety Services (CoSS) Team is looking for a smart and creative Applied Scientist to apply and extend state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model to join the Applied Science team. At Amazon, we are working to be the most customer-centric company on earth. Millions of customers trust us to ensure a safe shopping experience. This is an exciting and challenging position to drive research that will shape new ML solutions for product compliance and safety around the globe in order to achieve best-in-class, company-wide standards around product assurance. You will research on large amounts of tabular, textual, and product image data from product detail pages, selling partner details and customer feedback, evaluate state-of-the-art algorithms and frameworks, and develop new algorithms to improve safety and compliance mechanisms. You will partner with engineers, technical program managers and product managers to design new ML solutions implemented across the entire Amazon product catalog. Key job responsibilities As an Applied Scientist on our team, you will: - Research and Evaluate state-of-the-art algorithms in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model. - Design new algorithms that improve on the state-of-the-art to drive business impact, such as synthetic data generation, active learning, grounding LLMs for business use cases - Design and plan collection of new labels and audit mechanisms to develop better approaches that will further improve product assurance and customer trust. - Analyze and convey results to stakeholders and contribute to the research and product roadmap. - Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research - Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software - Publish research publications at internal and external venues. About the team The science team delivers custom state-of-the-art algorithms for image and document understanding. The team specializes in developing machine learning solutions to advance compliance capabilities. Their research contributions span multiple domains including multi-modal modeling, unstructured data matching, text extraction from visual documents, and anomaly detection, with findings regularly published in academic venues.

We are looking for an seasoned Applied Scientist to design, build, and deploy the ML/AI models that power our decision intelligence platform. You will work at the intersection of causal inference, time-series forecasting, anomaly detection, and LLM-based reasoning — all applied to real operational problems with measurable business impact. Key Job Responsibilities Decision Intelligence Models - **Causal inference & root cause analysis:** Build models that decompose fleet-wide metric movements into root causes, distinguishing correlation from causation across operational dimensions (site, service, failure mode, time) - **Dose-response modeling:** Develop models that learn the quantitative relationship between intervention intensity and outcome magnitude - **Forecasting & projection:** Build time-series models that project metric trajectories under different intervention scenarios, enabling "if we do X, expect Y by date Z" recommendations - **Anomaly detection & trend identification:** Develop multi-variate anomaly detection that distinguishes signal from noise in noisy operational data, and identifies emerging patterns before they become crises - **Confidence calibration:** Build and maintain calibrated confidence scores for recommendations, ensuring the system knows what it knows and what it doesn't - **Outcome attribution:** Design experiments and causal methods to measure the true impact of interventions LLM Integration & Reasoning - **Structured reasoning:** Design LLM prompting architectures that reliably transform operational data into executive-quality narrative summaries, decision framings, and recommendation rationales - **LLM evaluation:** Build evaluation frameworks that measure LLM output quality (accuracy, actionability, calibration) and detect degradation over time - **RAG systems:** Design retrieval-augmented generation systems that ground LLM outputs in operational data, historical playbooks, and institutional knowledge - **Progressive autonomy:** Design the trust-calibration system where AI gradually earns expanded authority based on demonstrated accuracy over time Research & Production - **End-to-end ownership:** Take models from research through production deployment — you ship, you monitor, you iterate - **Experimentation:** Design A/B tests and quasi-experiments to validate model improvements and measure business impact - **Stakeholder communication:** Translate complex scientific results into actionable insights for non-technical senior leaders

Would you like to work on developing AI systems models for one of the largest transactional distributed systems in the world? How about working with customers and peers from the entire range of Amazon's business and Science on complex real world problems, backed with high volume data ? Whether you're passionate about building highly scalable and reliable systems or a scientist who likes to solve business problems, Amazon Tax Platform Services is the place for you. We are responsible for the tax calculation platform, providing the core services that calculate taxes (sales tax and VAT) for all Amazon sales, physical and digital, globally. We seek to provide the correct tax amounts to the customer when placing their Amazon order, and ensure all records are stored safely to meet tax law requirements around the globe. Our challenges include staying on top of the complex and ever-changing global tax legislations as well as computing calculations correctly and quickly, thousands of times a second, and each one needs to be accurate. As an Applied scientist, you will provide machine learning leadership to the team that helps increase the accuracy of Tax classification based product information in Amazon catalogue making it the biggest and most challenging tax classification using Machine learning models globally. You will work with large language models, to build various machine learning models to predict accuracy of human's on specific tasks, reason with large volumes of systems and legislative changes to identify causal determinants, apply generative AI to model outcomes from sparse data. You will help us innovate different ways to enhance tax classification experience for our global customers. You will need to be entrepreneurial, work in a highly collaborative environment with SDEs, Product managers and businesses. We like to move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality.

We are seeking a Sr. Data Scientist, to join Amazon Publisher Cloud (APC) team. APC is a game-changing new service, that operates at the intersection of advertising, secure signal collaboration, and cloud services. In this role you will be a key member of an entrepreneurial team tackling some of the most exciting and complex opportunities in digital advertising. The ideal candidate will have deep curiosity and creativity to build data science based solutions. Should demonstrate ability to incorporate latest tech, tools, and methodologies like generate AI and LLMs to enhance our product offerings. Lastly, have experience and comfort working with product, engineering, and Data Science peers within and across teams. Key job responsibilities In this role, you'll develop sophisticated methodologies and models that enable secure data collaboration between publishers and advertisers while maintaining privacy and data governance standards. - Design and implement experimental frameworks to validate Clean Room collaboration methodologies and measure their effectiveness - Develop statistical models and algorithms that power privacy-preserving data analysis capabilities - Lead end-to-end analysis projects to derive actionable insights from cross-party data collaborations - Partner with product teams to translate business requirements into technical solutions - Create reproducible analysis frameworks and documentation A day in the life - Meet with Product Managers to scope requirements for a new cross-publisher audience overlap analysis feature - Write and test code for a new measurement methodology that enables stakeholders to understand campaign effectiveness while preserving user privacy - Design and document statistical approaches for a new clean room collaboration use case - Conduct deep-dive analyses and run experiments to validate new methodologies against industry standards - Present findings and recommendations to leadership and stakeholders - Contribute to the team's technical roadmap and architecture decisions About the team APC Team comprises of smart, skilled, and driven professionals across leadership, product, engineering, data science, business development, and customer success function. We sit within Amazon Publisher Services (APS) business, which helps digital publishers around the world build and grow thriving businesses. We provide services and advanced technologies to web, mobile app and advanced TV publishers of all sizes, including many of comScore’s global top 100, to help them monetize their content with demand from multiple programmatic buyers.

The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is seeking a Senior Applied Scientist to research and design cryogenic control hardware for large-scale quantum processors, with an emphasis on superconducting digital circuits. Controlling and reading out orders of magnitude more qubits than today's systems demands rethinking where classical control lives and what technology implements it; you will design and model superconducting digital logic (e.g., single-flux-quantum-family circuits) and related cryogenic electronics, evaluate them against the wiring, power, and thermal budgets of a scaled system, and co-design control approaches with the qubit devices they serve. You will carry designs from concept through simulation, layout, design review, and fabrication handoff, and your evaluations will inform which control-electronics directions the organization invests in. Key job responsibilities • Design and simulate superconducting digital circuits and cryogenic control/readout electronics for scalable qubit control • Develop and validate Josephson-junction circuit models, timing and margin analyses, and verification methodology for superconducting digital designs • Quantify system-level trade-offs — wiring count, power dissipation at each cryogenic stage, latency, footprint — across candidate control architectures, and communicate recommendations • Co-design control interfaces with qubit-device colleagues so control electronics and quantum devices are developed as one system • Deliver design artifacts through the organization's design-request and design-review processes, including tapeout-ready layouts and verification reports • Present results internally and, where appropriate, publish in peer-reviewed venues About the team The Enabling Technologies team develops technologies and relationships facilitating scaling, including alternative quantum processor architectures, superconducting qubit device packaging, and cryogenic control device electronics. We work alongside a co-located quantum processor design team focused on current-generation devices; the two teams share design and review processes, and moving ideas between them is an explicit part of our charter.