Careers

Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos). If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! Prime Air's Flight Sciences High-Fidelity Methods (HFM) team is seeking an outstanding Applied Scientist to develop and verify drone systems models and flight physics models. These models form the backbone of every flight simulation performed within Prime Air, directly informing aircraft design, system verification, certification, and business decisions. The HFM team's work enables prediction of critical vehicle performance metrics—including range, maneuverability, tracking error, and aircraft stability—and connects vehicle design and operational decisions to business outcomes such as customer reachability. Our models are also a crucial input to the design of flight control algorithms and software verification. Because the accuracy and reliability of these models underpin so many facets of Prime Air's mission, the scientist in this role will have broad, high-visibility impact on the program's success. Key job responsibilities The Applied Scientist in this role will own the end-to-end lifecycle of simulation models—from development and deployment through verification and ongoing maintenance. This begins with gathering downstream customer needs, selecting the most suitable modeling approach and level of fidelity, and coordinating the generation of input data. It extends through model training, development and maintenance of software interfaces, and verification of model accuracy. A core aspect of this role is determining the right modeling approach for a given physical phenomenon. The scientist will need a working understanding of the physics and systems being modeled, including vehicle aerodynamics, propeller performance, multibody dynamics, atmospheric physics, electric powertrain components, and guidance and navigation system (GNS) sensors. They will design experiments to generate the data needed to build and verify models, and apply uncertainty quantification and statistical analyses to validate model fidelity.

We are seeking a Senior Applied Scientist to join our team in developing pioneering AI research, Generative AI, Agentic AI, Large Language Models (LLMs), Diffusion and Flow Models, and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the AI Lab Team. This role offers a unique opportunity to work on AI research and AI products that will shape the future of online shopping experiences. Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers. As a Senior Applied Scientist, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation. Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store. Key job responsibilities - Design and implement novel AI solutions for Amazon catalog of products - Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models - Build and deploy autonomous AI Agents in Amazon production ecosystem - Scale AI models to handle billions of diverse products across multiple languages and geographies - Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning - Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem - Contribute to the scientific community through publications and conference presentations

Here's the job description with causal ML woven in: We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.

Amazon Customer Service (CS) Data Intelligence builds the data and Artificial Intelligence (AI) foundations for CS to ensure Amazon delivers the best customer service possible. CS Economics sits within CS DI and contributes to the CS knowledge base and decision frameworks. CS Economics seeks economists to apply economic methods to solve business problems. The ideal candidate will work with engineers and applied scientists to design models that leverage large scale and unstructured data, design scalable agents for non-tech CS partners to understand the impact of their actions, and propose mechanism designs to robustly match customers to our services. CS Economics is looking for optimistic critical-thinkers who combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates enjoy working in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities - Design and conduct rigorous evaluations of CS actions - Develop experiments to evaluate product launches - Communicate complex findings to business stakeholders in clear, actionable terms - Work with engineering teams to develop scalable tools that automate and streamline evaluation processes A day in the life Work with teammates to apply economic methods to business problems, e.g., identify the appropriate research question and identification strategy, write code to estimate heterogeneous treatment effects or conduct experiment analysis, write and present a document with findings to business leaders. We collaborate with partner teams within and outside of CS throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team Amazon Customer Service (CS) Economics provides estimates and measures of the causal impact of CS actions on costs and benefits. We build agents and guide leadership to establish processes to scale valid experimentation, causal inference, and mechanism design.

Amazon Security is seeking an Applied Scientist to work on GenAI acceleration within the Secure Third Party Tools (S3T) organization. The S3T team has bold ambitions to re-imagine security products that serve Amazon's pace of innovation at our global scale. This role will focus on leveraging large language models and agentic AI to transform third-party security risk management, automate complex vendor assessments, streamline controllership processes, and dramatically reduce assessment cycle times. You will drive builder efficiency and deliver bar-raising security engagements across Amazon. Key job responsibilities Own and drive end-to-end technical delivery for scoped science initiatives focused on third-party security risk management, independently defining research agendas, success metrics, and multi-quarter roadmaps with minimal oversight. Understanding approaches to automate third-party security review processes using state-of-the-art large language models, development intelligent systems for vendor assessment document analysis, security questionnaire automation, risk signal extraction, and compliance decision support. Build advanced GenAI and agentic frameworks including multi-agent orchestration, RAG pipelines, and autonomous workflows purpose-built for third-party risk evaluation, security documentation processing, and scalable vendor assessment at enterprise scale. Build ML-powered risk intelligence capabilities that enhance third-party threat detection, vulnerability classification, and continuous monitoring throughout the vendor lifecycle. Coordinate with Software Engineering and Data Engineering to deploy production-grade ML solutions that integrate seamlessly with existing third-party risk management workflows and scale across the organization. About the team Security is central to maintaining customer trust and delivering delightful customer experiences. At Amazon, our Security organization is designed to drive bar-raising security engagements. Our vision is that Builders raise the Amazon security bar when they use our recommended tools and processes, with no overhead to their business. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and ASR, TTS, & Speech to Speech models, requiring foundational deep learning and generative models knowledge. Applied scientists will contribute to cross-team scientific efforts, collaborate with partner teams, and deliver solutions 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 and implement algorithms and modeling techniques to advance the state of the art with LLMs, particularly contributing to scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a foundational understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in a fast-paced environment, like to tackle complex challenges, and are eager to deliver impactful solutions while iterating based on user feedback. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build and support online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Work on ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Fine-tune/post-train LLMs using techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer's location and language. * Experiment and help set up experimentation frameworks for agile model and data analysis or A/B testing. * Contribute to research efforts that drive innovation forward. * Collaborate with cross-team scientists and engineers on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems. * Contribute to end-to-end delivery of scientific solutions from research to production, including reusable science components and services. * Communicate solutions clearly to peers, partners, and stakeholders. * Actively participate in the broader internal and external scientific community through publications and community engagement.

How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promise, across hundreds of millions of packages daily? SCOT Fulfillment Optimization (FO) owns the science behind these decisions. We are seeking Applied Scientists to join the FO Science & Tech team in Luxembourg (alternatively: Barcelona, or London). You will design and build optimization models that power Amazon's fulfillment decisions at scale from real-time order assignment and multi-objective cost-speed tradeoffs to capacity-aware control systems that steer millions of shipments per hour toward operational plans. Basic qualifications * PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience) * Strong programming skills (Python preferred; experience with optimization solvers a plus) * Research experience in one or more: * Large-scale mathematical programming (LP, MIP, decomposition methods) * Combinatorial optimization (assignment, scheduling, network flows) * Multi-objective optimization and control Preferred qualifications * Experience building optimization systems that run in production at scale * Being comfortable with ambiguity and fast iteration cycles * Publications in relevant venues Key job responsibilities Design and implement optimization models (MIP, heuristics, decomposition) that solve large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily. A day in the life You formulate an optimization problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live. About the team SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.

We are seeking a Sr. Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions 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.

Do you want to work on building state-of-the-art recommendation systems and personalization engines at scale to transform how millions of customers discover products, content, and experiences? Come join the world-class researchers and academics in the AWS AI endeavor, and develop the science that powers personalized experiences for countless businesses in cloud computing! AWS is the world-leading provider of cloud services, and Amazon Personalize brings the same machine learning technology used by Amazon.com to developers everywhere. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world personalization challenges, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and journals. The scientific topics you are going to work on include, but are not limited to: large-scale recommendation and ranking models, sequential and session-based recommendation, cold-start and few-shot personalization, contextual and real-time recommendations, representation learning for users and items, and the integration of foundation models and LLMs into recommendation pipelines, etc. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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. Mentorship and 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. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Data Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing models that aids in audience segmentation, AI-enablement in data/reporting, and assessing randomized controlled trials to rate marketing effectiveness. This role is suitable for candidates with strong background in cohort analysis, causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal & Predictive Models Build and validate causal and predictive models that quantify how marketing and lifecycle programs affect customer retention, engagement, and subscriber growth for Amazon Music. Own the team's models end to end. Statistical Analysis at Scale Analyze large customer datasets using SQL and Python or R to interpret results and uncover meaningful patterns, grounding your work in trusted, well-governed data. Enable Data-Driven Decisions Partner with marketing and finance stakeholders to deliver recommendations that improve retention and return on investment. Prioritize the work with the greatest impact on customer growth, and present findings clearly to both technical and executive audiences. Bring AI into Self-Service Tools Partner with product managers and engineers to incorporate AI and machine learning into the team's self-service analytics tools, so stakeholders can answer their own questions faster and at greater scale. Cross-Functional Problem Solving Work across marketing, product, and engineering teams to frame key business questions and build credible analytical solutions. Innovate & Document Track emerging methods and apply them to improve measurement; maintain reproducible documentation and clear reporting for non-technical audiences.