Careers

We are a passionate team applying the latest advances in technology to solve real-world challenges. As a Data Scientist working at the intersection of machine learning and advanced analytics, you will help develop innovative products that enhance customer experiences. Our team values intellectual curiosity while maintaining sharp focus on bringing products to market. Successful candidates demonstrate responsiveness, adaptability, and thrive in our open, collaborative, entrepreneurial environment. Working at the forefront of both academic and applied research, you will join a diverse team of scientists, engineers, and product managers to solve complex business and technology problems using scientific approaches. You will collaborate closely with other teams to implement innovative solutions and drive improvements. At Amazon, we cultivate an inclusive culture through our Leadership Principles, which emphasize seeking diverse perspectives, continuous learning, and building trust. Our global community includes thirteen employee-led affinity groups with 40,000 members across 190 chapters, showcasing our commitment to embracing differences and fostering continuous learning through local, regional, and global programs. We prioritize work-life balance, recognizing it as fundamental to long-term happiness and fulfillment. Our team is committed to supporting your career development through challenging projects, mentorship opportunities, and targeted training programs that help you reach your full potential. Key job responsibilities Work hands-on with complex, noisy datasets to derive actionable insights and explain/debug black-box models using interpretability and data-attribution methods (e.g., SHAP/TreeSHAP, Anchors, Integrated Gradients, counterfactuals, nearest-neighbor exemplars, influence/data attribution). Design and analyze experiments and observational studies with rigorous statistical inference, including confidence intervals, power/sample-size estimation, variance reduction, and appropriate tests (e.g., two-sample tests, permutation tests, sequential testing, and multiple-comparison control such as FDR). Benchmark models and datasets using classical and modern techniques; select ML methods based on data and operational constraints (e.g., clustering/KDE, tree ensembles, CNN/RNN/Transformers, representation learning), and evaluate with robust metrics and diagnostics (e.g., AUROC, AUPRG, proper scoring rules/losses, calibration/ECE, threshold/utility curves, slice-based evaluation, and error analysis). Apply production-grade measurement and MLOps practices, including data quality monitoring, drift/shift detection (PSI, KS, MMD/embedding drift), and A/B test design and readouts with disciplined diagnosis of metric movement (e.g., instrumentation changes, seasonality, novelty effects, sample-ratio mismatch, guardrail tradeoffs). Deliver end-to-end analyses that improve team execution and decision-making—define goal-driving metrics with stakeholders, build clear reporting (tables, dashboards, and visualizations), and communicate results that translate into concrete actions. Investigate anomalies and data integrity issues across diverse data sources using structured root-cause analysis, correlation diagnostics, significance testing, and simulation across high- and low-fidelity datasets. Partner closely with cross-functional domain experts to design experiments and interpret results, applying modern statistical methods to evaluate predictive and generative models as well as operational and process performance. Develop production-quality analytics and modeling code—write well-tested, maintainable SQL/Python scripts and analysis workflows that can be promoted into production pipelines, and continuously adopt new statistical methods and best practices as the field evolves. A day in the life New data has just landed and promoted to our datalake. You load the data and verify it's overall integrity by visualizing variation across target subsets. You realize we may have made progress toward our goals and begin to test the validity of your nominal results. At midday you grab lunch with new coworkers and learn about their fields or weird interests (there are many). You generate visualizations for the entire dataset and perform significance tests that reinforce specific findings. You meet with peers in the afternoon to discuss your findings and breakdown the remaining tasks to finalize your group report! About the team Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you.

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

Join Amazon Pharmacy as the founding engineering leader for our Supply Chain technology team in Bangalore. You will build and lead a team of engineers responsible for the systems that determine what medications to buy, where to place inventory, and how to plan capacity across Amazon Pharmacy's fulfillment network. This is a greenfield opportunity to architect ML-driven supply chain systems from the ground up, leveraging Amazon's cloud-native infrastructure, proven supply chain optimization patterns, and operations research best practices at Amazon scale. You will own the full supply chain stack for Amazon Pharmacy: demand forecasting, procurement optimization, inventory placement, resource planning, and Sales & Operations Planning (S&OP). Your systems will directly determine whether a patient's medication is in stock, at the right facility, at the right time. The stakes are high: pharmacy supply chains operate under regulatory constraints, drug expiry windows, and prescription-driven demand signals that make this one of the most technically interesting supply chain problems at Amazon. We are building an AI-native engineering organization. You will operate with a flat structure, leading senior ICs directly, and leveraging AI-augmented development workflows (code generation, automated testing, ML-driven monitoring) to move fast with a lean team. If you are energized by building ML-intensive systems, leading from the front technically, and setting the culture for a high-autonomy engineering team, this is your role. Key job responsibilities A. Engineering Leadership & Team Building • Lead a team of engineers building ML-driven and optimization-based supply chain systems • Hire engineers who can operate at the intersection of software engineering and quantitative methods • Define the technical and science roadmap: identify high-impact modeling opportunities across demand forecasting, procurement, placement, and planning • Set the bar for scientific rigor: reproducibility, offline evaluation, backtesting, and experiment design • Mentor engineers on translating quantitative methods into production-ready systems • Manage the team's portfolio of work, balancing near-term production improvements with longer-term capability building B. Applied Science & Operations Research • Design demand forecasting systems: time series methods, probabilistic forecasting, hierarchical models that handle sparse pharmacy SKU-level demand • Develop optimization models for procurement: cost minimization under lead time uncertainty, expiry constraints, supplier capacity, and regulatory requirements • Design placement and allocation algorithms: multi-facility inventory optimization, safety stock computation, transfer policies • Apply operations research techniques: linear and integer programming, stochastic optimization, dynamic programming, simulation, multi-objective optimization • Develop capacity and resource planning models: labor demand forecasting, throughput optimization, shift planning • Translate scientific methods into engineering designs that your team can build, test, and deploy C. Production & Experimentation • Own the full system lifecycle: development, offline evaluation, online experimentation, deployment, and production monitoring • Design experimentation frameworks for supply chain interventions where traditional A/B testing is difficult (counterfactual evaluation, synthetic controls, switchback experiments) • Build backtesting and simulation infrastructure to evaluate model performance against historical data before deployment • Define APIs, latency requirements, failure modes, and monitoring dashboards for your team's systems • Establish performance metrics and review cadence to ensure systems improve over time and degrade gracefully D. Collaboration & Influence • Partner with peer SDMs across the supply chain org to align on architecture, interfaces, and priorities • Work with product managers to translate business problems into well-defined optimization objectives • Collaborate across time zones with US-based science and product teams on priorities and research direction • Represent the team in technical and science reviews • Influence the broader supply chain engineering roadmap through data-driven insights and scientific recommendations

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

Are you passionate about helping customers achieve business transformation through AI? Do you want to lead forward-deployed teams that embed directly into the enterprise and unlock real business outcomes? And are you ready to operate as a general manager across engineering, science, and commercial strategy in the fastest-moving space in AI and infrastructure? The AWS Generative AI Innovation Center (GenAIIC) is on a mission to accelerate enterprise AI transformation across global customers going from beyond isolated use cases to holistic, C-suite-sponsored initiatives that reshape how organizations operate. We combine deep AI expertise across science, strategy, and business transformation. We start with the customer's most critical operational challenges and work backwards, and deploy multidisciplinary teams that embed with the customer, prove impact in 45-day sprints, and expand across the enterprise. We are a fast-moving, entrepreneurial team that values leaders who can operate across technical depth and commercial breadth. As the Head of Innovation and Transformation, GenAIIC APJC Geo, you will own the end-to-end success of AI transformation engagements across the Asia Pacific, Japan, and Greater China region. You will lead a team of ML engineers, AI scientists, and AI strategists who work alongside customers to architect and deliver AI solutions that move and stay in production, realizing value. You will regularly engage with CFOs, CIOs, and C-suite executives — selling transformation, not technology. You must bring a GM mindset: equal fluency in engineering, applied science, go-to-market, and customer delivery. You are ready to roll up your sleeves alongside the team — whether that means scoping an agentic AI architecture, presenting to a board, or operationalizing a repeatable delivery motion across geos. You will partner with customers, AWS Sales, AWS service teams, AWS industry teams and AWS Professional Services delivery teams to meet the specific needs of the customer, and extend that use to other customers. Key job responsibilities Lead a high-performing, multidisciplinary team of ML engineers, AI scientists and strategists. Own the regional engagement pipeline — identify, qualify, and land C-suite-sponsored AI transformation programs with enterprise customers. Guide the team in designing, building, and deploying production AI solutions. Drive a land-and-expand motion: prove value in focused sprints, then scale across business units with executive sponsorship. Operate as a GM — balancing technical delivery, talent development, commercial outcomes, and operational excellence. Advise and influence customers on enterprise AI strategy, including architecture decisions, organizational readiness, and change management. Drive adoption of AWS generative AI services by delivering AI expertise and developing repeatable transformation playbooks. Develop new strategies and mechanisms to address evolving customer needs while increasing business velocity and impact for AWS. Influence product roadmap with service teams based on customer feedback. A day in the life 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.

Come build a future with AWS Economic Development Research & Analysis Team Every year, AWS invests billions of dollars around the world in cloud computing infrastructure, data centers, and education for cloud-skilled professions. These investments shape the growth of economies and our global and local ability to meet the demands of the digital revolution. If you're ready to be a key part of the AI/ML revolution, join us and see what it is like to transform tech infrastructure first-hand. As AWS's strategic economic storytellers, the AWS Economic Development Research & Analysis (R&A) team crafts global, data-driven narratives spanning three strategic categories: • Core Research – Economic Impact Studies measuring GDP and job contributions; 15-year Investment Forecasts tied to specific build plans; Fiscal Impact Analyses quantifying tax revenues; and Labor Demand Analyses enabling workforce partnerships • Specialized Research – Energy, water, fiber, and other infrastructure-adjacent areas • Hyper-Local Intelligence – Granular socioeconomic analyses enabling community-level storytelling Amazon is seeking a high-judgment, agile, data-driven, and highly motivated Principal Economist to join this lean, high-impact team. You will lead the development of public-facing research that tells compelling narratives around AWS's economic impact through AI/ML infrastructure, while supporting original research on the downstream effects of cloud computing and AI adoption — including hyper-localized analyses and energy-related research. In this role, you’ll be both a poet and an engineer: rigorous in quantitative methods, fluent in economic storytelling, and experienced with financial and/or accounting datasets. Key job responsibilities Key Job Responsibilities • Own end-to-end development of public-facing studies on a global scale and provide economic, fiscal, labor, and market research to support infrastructure expansion and planning • Establish and maintain operating and business metrics to evaluate economic, social, and environmental impact across regions • Drive analytical excellence, analyzing and extracting insights from both structured and unstructured data to optimize and scale key research processes, including leveraging AI-powered tools • Partner with Legal, Finance, Tax, Compliance, Investor Relations, Public Policy, Public Relations, and Marketing teams to validate research frameworks, align on benchmarks, and frame measures of AWS investment and impact • Present summaries of financial and business data insights to senior leadership; create FAQs; occasionally travel to present research and support local public policy and public relations stakeholders • Own research vertical(s) – Lead the development and impact of an actionable body of research in at least one of the following areas: energy and water, hyper-localized socioeconomic impact, AI/ML downstream effects, or broader infrastructure • Provide thought leadership and mentorship within the team, helping to scale research capacity and quality across a growing global portfolio A day in the life You will work closely with finance, infrastructure, energy, and business development teams, as well as AWS Public Policy, Investor Relations, Public Relations, Marketing, and Sales. With AWS infrastructure as your primary customer, you will gain deep exposure to AWS's global business and operate in a highly cross-functional environment. This role is ideal for someone with intellectual curiosity, strong data skills, and a passion for applying rigorous economic methods to drive high-impact business outcomes at the intersection of finance, economics, and public policy. About the team AWS Economic Development is part of the AWS Global Public Policy organization. We work seamlessly with public policy teammates, internal customers, and colleagues across Amazon to realize the AWS global expansion strategy and build collaborative partnerships in our communities. The R&A team serves as the analytical backbone of this mission.

Are you interested in big data, machine learning, LLM, and product recommendations? If so, Amazon's Personalization team might be the right place for you. About our organization: We are part of Amazon’s Personalization organization, a high-performing group with a huge impact on hundreds of millions of customers, innovating at the intersection of customer experience, machine learning, and large-scale distributed systems. We run global experiments and our work has revolutionized e-commerce with features such as "Compare with similar items", "Keep shopping for ...", “Customers who bought this item also bought”, and, “Frequently bought together” among others. Amazon’s internal surveys regularly recognize us as one of the best organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continuous opportunity to learn and grow. About you: You are an Applied Scientist who loves big data and passionate about improving customer shopping experience by inventing and applying state-of-art technologies (e.g., LLM, Machine Learning, NLP, and Computer Vision) to build the next-generation product recommendation engine for Amazon. You have an entrepreneurial spirit, know how to deliver, are deeply technical and highly innovative. You work closely with software engineers to put algorithms into production. You also work in partnership with teams across Amazon to create enormous benefits for our customers. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of products used every day by people you know. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes Design, development and evaluation of highly innovative models for predictive learning Work closely with software engineering teams to drive model implementations and new feature creations Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Research and implement novel machine learning and statistical approaches Mentor junior scientists; review their work and provide feedback About the team Our mission is to delight every Amazon customer with a personalized shopping experience. We achieve our mission through investments in large-scale machine learning and distributed system solutions with the purpose of delivering the future of shopping on Amazon. Our solutions help customers explore product categories, discover high quality products that meet their needs, and provide most relevant information to help customers make confident shopping decisions. We are seeking an Applied Scientist to make step function improvements in creating a delightful shopping experience.

Employer: Amazon Development Center U.S., Inc. Position: Applied Scientist II - AMZ27057.1 Location: Boston, MA Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $161803 - $193200) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Set up experimentation frameworks for agile model analysis and A/B testing. * Collaborate with partner teams on LLM evaluation frameworks and post-training methodologies. * Contribute to end-to-end delivery of solutions from research to production, including reusable science components. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Set up experimentation frameworks for agile model analysis and A/B testing. * Collaborate with partner teams on LLM evaluation frameworks and post-training methodologies. * Contribute to end-to-end delivery of solutions from research to production, including reusable science components. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.