Careers

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. 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 - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists

The Measurement, Ad Tech, and Data Science (MADS) team at Amazon Ads is at the forefront of developing innovative solutions that help tens of millions of advertisers understand the value of their ad spend while prioritizing customer privacy and measurement quality. The Media Planning Science team develops and implements models that deliver insights and recommendations for strategic media planning and measurement across Amazon Advertising's product portfolio. Our mission is to help advertisers create and execute plans that meet their objectives while providing accurate measurement tools. We work on a multitude of problem statements that encompass Reach and Frequency, Budget Planning Optimization, and Recommendations. Our models leverage both heuristic and machine learning approaches including deep learning techniques, with insights delivered through agent-based tools and APIs that integrate seamlessly into user interfaces and programmatic systems to ensure optimal advertising outcomes. As an Applied Scientist on the team, you will be at the forefront of innovation, developing media planning solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities * Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement and media planning problems * Disambiguate problems to propose clear evaluation frameworks and success criteria * Work autonomously and write high quality technical documents * Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production * Partner closely with other scientists to deliver large, multi-faceted technical projects * Share and publish works with the broader scientific community through meetings and conferences * Communicate clearly to both technical and non-technical audiences * Contribute new ideas that shape the direction of the team's work * Mentor more junior scientists and participate in the hiring process A day in the life You will solve real-world problems by analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop ML/DL models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal documents to share insights to audiences of varying levels of technical sophistication. About the team We are a team of scientists across Applied, Research, and Data Science disciplines. You will work with colleagues with deep expertise in ML, DL, NLP, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

As a Sr. Applied Scientist, you will be responsible for bringing new product designs through to manufacturing. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: * Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes * Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks * Establishing scalable, efficient, automated processes to handle large scale design and data analysis * Conducting research into use conditions, materials and analysis techniques * Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis * Developing, implementing guidelines to continually optimize design processes * Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design * Using of programming languages like Python and Matlab for analytical/statistical analyses and automation * Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials * Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation * Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques

Amazon's AGI Information is seeking an exceptional Applied Scientist to drive science advancements in the Amazon Knowledge Graph team (AKG). AKG is re-inventing knowledge graphs for the LLM era, optimizing for LLM grounding. At the same time, AKG is innovating to utilize LLMs in the knowledge graph construction pipelines to overcome obstacles that traditional technologies could not overcome. As a member of the AKG IR team, you will have the opportunity to work on interesting problems with immediate customer impact. The team is addressing challenges in web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory operating over Graphs. You will also have the opportunity to work with scientists working on the other challenges, and with the engineering teams that deliver the science advancements to our customers. A successful candidate has a strong machine learning and agent background, is a master of state-of-the-art techniques, has a strong publication record, has a desire to push the envelope in one or more of the above areas, and has a track record of delivering to customers. The ideal candidate enjoys operating in dynamic environments, is self-motivated to take on new challenges, and enjoys working with customers, stakeholders, and engineering teams to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems. You will collaborate with applied scientists and engineers to develop novel algorithms and modeling techniques to build the knowledge graph that delivers fresh factual knowledge to our customers, and that automates the knowledge graph construction pipelines to scale to many billions of facts. Your first responsibility will be to solve entity resolution to enable conflating facts from multiple sources into a single graph entity for each real world entity. You will develop generic solutions that work fo all classes of data in AKG (e.g., people, places, movies, etc.), that cope with sparse, noisy data, that scale to hundreds of millions of entities, and that can handle streaming data. You will define a roadmap to make progress incrementally and you will insist on scientific rigor, leading by example.

We are seeking an Applied Scientist II to join Amazon Customer Service's Science team, where you will build AI-based automated customer service solutions using state-of-the-art techniques in retrieval-augmented generation (RAG), agentic AI, and post-training of large language models. You will work at the intersection of research and production, developing intelligent systems that directly impact millions of customers while collaborating with scientists, engineers, and product managers in a fast-paced, innovative environment. Key job responsibilities - Design, develop, and deploy information retrieval systems and RAG pipelines using embedding models, reranking algorithms, and generative models to improve customer service automation - Conduct post-training of large language models using techniques such as Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO) to optimize model performance for customer service tasks - Build and curate high-quality datasets for model training and evaluation, ensuring data quality and relevance for customer service applications - Design and implement comprehensive evaluation frameworks, including data curation, metrics development, and methods such as LLM-as-a-judge to assess model performance - Develop AI agents for automated customer service, understanding their advantages and common pitfalls, and implementing solutions that balance automation with customer satisfaction - Independently perform research and development with minimal guidance, staying current with the latest advances in machine learning and AI - Collaborate with cross-functional teams including engineering, product management, and operations to translate research into production systems - Publish findings and contribute to the broader scientific community through papers, patents, and open-source contributions - Monitor and improve deployed models based on real-world performance metrics and customer feedback A day in the life As an Applied Scientist II, you will start your day reviewing metrics from deployed models and identifying opportunities for improvement. You might spend your morning experimenting with new post-training techniques to improve model accuracy, then collaborate with engineers to integrate your latest model into production systems. You will participate in design reviews, share your findings with the team, and mentor junior scientists. You will balance research exploration with practical implementation, always keeping the customer experience at the forefront of your work. You will have the autonomy to drive your own research agenda while contributing to team goals and deliverables. About the team The Amazon Customer Service Science team is dedicated to revolutionizing customer support through advanced AI and machine learning. We are a diverse group of scientists and engineers working on some of the most challenging problems in natural language understanding and AI automation. Our team values innovation, collaboration, and a customer-obsessed mindset. We encourage experimentation, celebrate learning from failures, and are committed to maintaining Amazon's high bar for scientific rigor and operational excellence. You will have access to world-class computing resources, massive datasets, and the opportunity to work alongside some of the brightest minds in AI and machine learning.

Do you want to create intelligent, adaptable robots with global impact? The Vulcan Stow team (https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem) at Amazon Robotics builds high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. We invent and deploy machine learning, optimization algorithms, and geometric reasoning models that empower robots to identify the best available placement opportunities, learn effective stow strategies, and continuously improve capacity utilization. Our mission is to maximize throughput developing intelligent policies that understand physical constraints, reason about geometric action opportunities, and select behaviors that achieve high-density, reliable stowing. We hire and develop ML experts in reinforcement learning, combinatorial optimization, predictive modeling, and decision systems. Our solutions learn from millions of stowing decisions to continuously improve warehouse capacity and throughput. We are seeking an experienced Applied Science Manager for Match and Affordances team to lead a team of talented applied scientists and engineers. You will drive ML innovation using the latest advancements in transformer-based architectures to enable maximum storage utilization, learn affordances and behaviors in high-density environments with the reliability and scale that delights our customers. Collaborating with cross-functional teams across perception, motion planning, and fulfillment operations, you will deliver scalable solutions that optimize stow strategy and warehouse capacity across geographies and conditions. Key job responsibilities People Leadership: Prioritize being a great people manager - motivating, rewarding, and coaching your diverse team is the most important part of this role. Recruit and retain top talent in machine learning, optimization, and decision systems. Excel in day-to-day people and performance management tasks. Technical Vision: Set a vision for your team and create technical roadmaps focused on stow policy development, placement optimization, and density improvements. Guide research, design, deployment, and evaluation of ML and RL algorithms, optimization methods, and geometric reasoning systems that inform robot action selection. Cross-functional Collaboration: Work closely with perception, motion planning, hardware, and fulfillment teams to create integrated solutions that maximize storage density while maintaining operational reliability. Partner with computer vision teams on 3D scene understanding and container geometry representation that drives policy learning. Delivery Excellence: Implement best practices in applied research and software development. Manage project timelines, resources, and deliverables effectively. Keep technical skills current to contribute meaningfully to architecture and design discussions. Problem Solving: Regularly participate in deep-dive troubleshooting exercises and drive technical post-mortem discussions to identify root causes of policy regressions, optimization failures, and KPI degradations. A day in the life - Prioritize being a great people manager: motivating, rewarding, and coaching your diverse team is the most important part of this role. You will recruit and retain top talent and excel in people and performance management tasks. - Set a vision for the team and create the technical roadmap that deliver results for customers while thinking big for future applications. - Guide the research, design, deployment, and evaluation of complex computer vision and machine learning algorithms for contact-rich, cluttered, real-world manipulation problems. - Work closely with motion, hardware, and software teams to create integrated robotic solutions that are better than the sum of their parts. - Implement best practices in applied research and software development, managing project timelines, resources, and deliverables effectively. Amazon offers a full range of benefits for 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: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 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 Watch this video to learn more about Vulcan program in Amazon Robotics: https://www.amazon.science/latest-news/how-amazon-robotics-researchers-are-solving-a-beautiful-problem

The Quantum Algorithms Team at the Amazon Web Services (AWS) Center for Quantum Computing (CQC) is looking to hire an Applied Scientist. In this role, you will conduct theoretical research into applications of fault-tolerant quantum computers and develop new quantum algorithms. You will work on the forefront of quantum computing research and contribute toward the development of useful quantum technology, working alongside theoretical and experimental scientists, engineers, and technicians across the CQC. You should have a deep and broad knowledge of quantum algorithms and applications for fault-tolerant quantum computers, and standard mathematical and computational methods for analyzing them. Key job responsibilities We are looking to hire an Applied Scientist focused on developing algorithms and applications for fault-tolerant quantum computers. In this role, you will: - Collaborate with other members of the Quantum Algorithms Team and our academic collaborators, to analyze and optimize existing quantum algorithms - Think big to invent new quantum algorithms and discover new application areas for fault-tolerant quantum computers - Provide subject matter expertise on the topic of quantum algorithms, and communicate the latest developments to the broader quantum computing team - Work with internal and external stakeholders to strengthen the long-term value proposition of quantum computing - Interact with the Quantum Error Correction team to understand the interplay between quantum algorithms and quantum error correction - Publish research papers in scientific journals and present at conferences - Mentor research interns working on scientific projects You are expected to develop and lead high-impact research projects that intersect with our engineering roadmap. Organization and communication skills are essential. A day in the life As an Applied Scientist on the Quantum Algorithms team, you develop practical applications for quantum computers. To do so, you will: - Read academic publications, understand their practical applicability, and present the results in internal meetings - Analyze and optimize existing quantum algorithms in domains such as quantum simulation, quantum chemistry, optimization, or machine learning - or develop entirely new algorithms - Perform resource estimates to quantify the cost of running quantum algorithms within a given hardware architecture - Write technical internal reports documenting research findings and analysis - Write research papers and present your research results at flagship academic and industry conferences.

As part of the Agentic WorkSpaces organization, we empower millions of professional users—spanning enterprises, research institutions, and government agencies—through AWS WorkSpaces, a cloud-based desktop and application management platform. Our mission is to redefine productivity for users navigating complex legacy application environments by deploying agentic AI solutions that bridge the gap between generative AI and real-world enterprise workflows. We combine Amazon’s deep operational expertise with a relentless focus on innovation to deliver AI experiences that are intuitive, scalable, and transformative. Where legacy systems pose unique challenges, we become the trusted partner for customers seeking to automate, optimize, and future-proof their workflows. The Senior Applied Scientist will lead the development of LLM-driven agentic systems tailored to AWS WorkSpaces environments. This role demands pioneering scientific approaches to solve unresolved problems at scale, balancing rapid iteration with rigorous evaluation. Success hinges on your ability to design, implement, and validate AI solutions that empower users to work smarter in legacy-heavy ecosystems. Key job responsibilities * Architect agentic AI systems that leverage LLMs, computer vision, and computer-use techniques to interact with legacy applications within AWS WorkSpaces. * Build evaluation frameworks to quantify agent performance, reliability, and user impact in real-world, unstructured environments. * Fine-tune and deploy domain-specific LLMs for workspace use cases, ensuring efficiency, safety, and alignment with enterprise requirements. * Collaborate cross-functionally with peer teams (e.g., troubleshooting, onboarding, security) to embed AI capabilities across the WorkSpaces product suite—from strategic visioning to hands-on implementation. * Drive operational excellence by delivering high-quality, production-ready science components that meet Amazon’s standards for robustness, reproducibility, and scalability. About the team Agentic WorkSpaces AI is the core science team within the Agentic WorkSpaces organization, dedicated to building LLM-driven agentic solutions that empower AWS WorkSpaces users—particularly in legacy application environments where modern AI struggles. We design, develop, and deploy intelligent agents leveraging LLMs, computer vision, and computer use to automate complex workflows. Collaborating closely with peer teams across troubleshooting, onboarding, and other critical areas, we integrate AI capabilities that enhance the entire WorkSpaces experience. Our work balances rapid innovation with rigorous evaluation and ethical alignment, delivering scalable, enterprise-ready solutions for millions of users worldwide.

WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing products that empower controllership, improve business decisions and financial planning by understanding financial drivers, and innovate science capabilities for efficiency and scale. We are looking for a data scientist to lead high visibility initiatives for forecasting Amazon Stores' financials. You will develop new science-based forecasting methodologies and build scalable models to improve financial decision making and planning for senior leadership up to VP and SVP level. You will build new ML and statistical models from the ground up that aim to transform financial planning for Amazon Stores. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to transform financial decision-making with science. The ideal candidate combines data-science acumen with strong business judgment. You have versatile modeling skills and are comfortable owning and extracting insights from data. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into business action. Key job responsibilities Demonstrating thorough technical knowledge, effective exploratory data analysis, and model building using industry standard ML models Working with technical and non-technical stakeholders across every step of science project life cycle Collaborating with finance, product, data engineering, and software engineering teams to create production implementations for large-scale ML models Innovating by adapting new modeling techniques and procedures Presenting research results to our internal research community

MULTIPLE POSITIONS AVAILABLE Employer: IMDB.COM, INC. Offered Position: Data Scientist III Job Location: Seattle, Washington Job Number: AMZ9971313 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range $165,006/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000