Careers

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

Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. The Agentic Customer Experience (ACX) org is responsible for weaving native-AI across the Connect application experiences delivered to end-customers, agents, and managers/supervisors. The Interactive AI Science team, serves as the cornerstone for AI innovation across Amazon Connect, functioning as the sole science team support high impact product including Amazon Q in Connect, Contact Lens and other key initiatives. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLM Agents evaluation and optimization, etc. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow and AWS Sagemaker. The ideal candidate has the ability to understand, implement, innovate on the state-of-the-art Agentic AI based systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. About the team Diverse Experiences AWS 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 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. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

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.

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.

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.

Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History. Key job responsibilities Key job responsibilities * Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms * Fundamentally understand Amazon’s underlying Neural Edge Engine to invent optimization techniques * Analyze deep learning workloads and provide guidance to map them to Amazon’s Neural Edge Engine * Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics * Train custom Gen AI models that beat SOTA and paves path for developing production models * Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects and product teams to build the best ML-centric solutions for our devices * Publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys.

Shopbop and Zappos are looking for a customer-obsessed Data Scientist to join the Customer Analytics organization. This role will be at the center of how we understand, reach, and serve our customers across every channel, not as a support function, but as a driving force behind our customer strategy. You will build the models that power personalization across sites, email, push, and paid media. You will design the causal frameworks that prove what's actually working versus what just looks like it is. You will apply machine learning, LLMs, and advanced optimization techniques to move us from intuition-driven decisions to evidence-driven ones at scale, across Shopbop and Zappos. The right candidate combines deep technical skills in machine learning and causal inference with genuine curiosity about customer behavior and retail dynamics. They thrive in ambiguity, move fluidly between model development and business strategy, and communicate complex findings clearly to both technical and non-technical audiences. They should have a collaborative mindset that enables them to work effectively across Lifecycle Marketing, Merchandising, Product, Engineering, and other cross-functional partners. This position sits within the Customer Experience organization. Key job responsibilities Design, build, and iterate on customer segmentation models that drive product recommendations, content ranking, intent detection, and customer-specific experiences on site, in email, and in push notifications across Shopbop and Zappos. Apply advanced optimization techniques — including uplift modeling, to improve real-time decisioning across marketing, digital, and channel experiences. Apply causal inference methods grounded in econometric and machine learning frameworks, including EconML, DoWhy, and CausalML, to estimate the true incremental lift of personalization strategies and marketing interventions through techniques such as double machine learning, meta-learners (T-learner, S-learner, X-learner), and targeted maximum likelihood estimation. Build and maintain predictive models for customer preferences and individualized treatment effect models that inform business strategy and investment decisions. Collaborate with Engineering to build scalable data pipelines, feature stores, and real-time serving infrastructure that support ongoing model development and experimentation. Partner with engineering teams to deploy data science models and solutions into production across email, site, and paid media channels, ensuring models translate from development into customer-facing impact. Translate complex analytical and modeling results into clear, actionable recommendations for leadership and cross-functional stakeholders, influencing strategy through evidence rather than intuition.

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

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communications Engineer in Modeling and Simulation, this role is primarily responsible for the developing and analyzing high level system resource allocation techniques for links to ensure optimal system and network performance from the capacity, coverage, power consumption, and availability point of view. Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define novel wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance, such as overall throughput, latency, system availability, packet loss, etc., as well as compatibility for both connectivity and interference mitigation with other space and terrestrial systems. This role in particular will be responsible for 1) evaluating complex multi-disciplinary trades involving RF bandwidth and network resource allocation to customers, 2) understanding and designing around hardware/software capabilities and constraints to support a dynamic network topology, 3) developing heuristic or solver-based algorithms to continuously improve and efficiently use available resources, 4) demonstrating their viability through detailed modeling and simulation, 5) working with operational teams to ensure they are implemented. This role will be part of a team developing the necessary simulation tools, with particular emphasis on coverage, capacity, latency and availability, considering the yearly growth of the satellite constellation and terrestrial network. 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. Key job responsibilities • Work within a project team and take the responsibility for the Leo's overall communication system design and architecture • Extend existing code/tools and create simulation models representative of the target system, primarily in MATLAB • Design interconnection strategies between fronthaul and backhaul nodes. Analyze link availability, investigate link outages, and optimize algorithms to study and maximize network performance • Use RF and optical link budgets with orbital constellation dynamics to model time-varying system capacity • Conduct trade-off analysis to benefit customer experience and optimization of resources (costs, power, spectrum), including optimization of satellite constellation design and link selection • Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements • Analyze and minimize potential self-interference or interference with other communication systems • Provide visualizations, document results, and communicate them across multi-disciplinary project teams to make key architectural decisions