Careers

Trusted by more startups around the world, AWS makes the power of cloud computing accessible for all by giving founders everywhere access to the same technology that powers the world's largest companies. With nearly two decades of experience supporting hundreds of thousands of startups, including 80% of unicorns, we democratize cloud computing to help founders bring their innovative ideas to life. We support founders at every stage of their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Data is central to how we do this: it helps us identify high-potential startups early, personalize the guidance we deliver, and prioritize where we can create the most value for founders and for AWS. We are seeking an Applied Science Manager to lead a team of applied scientists and analysts building the data and machine learning capabilities behind AWS Startups. You will own the science roadmap end-to-end, from the data foundation that unifies signals about founders, startups, and their products, through a portfolio of machine learning models, to the surfaces that put insights in the hands of the teams and products that serve startups. You will balance hands-on technical leadership with people management, setting the technical bar for your team while developing their careers. Key job responsibilities · Lead, coach, and grow a team of applied scientists, business intelligence engineers, and business analysts; hire and develop talent and set a high technical bar. · Own and prioritize the team's science roadmap and set technical direction for its machine learning models and data assets, balancing rapid experimentation with production quality, cost, and reliability. · Scope scientific projects, design and evaluate experiments, and productionize models that deliver measurable impact, establishing measurement, evaluation, and operational-excellence standards so quality and impact are quantified and defensible. · Drive the science behind recommendation systems, startup segmentation and targeting, and fraud detection, delivering models that surface relevant opportunities, group and prioritize startups by need and fit, and protect the business from fraud and abuse. · Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate strategy, results, and trade-offs clearly to technical and non-technical leaders. · Foster a culture of scientific rigor and rapid experimentation, and proactively identify and escalate risks with clear mitigation plans. About the team The AWS Startups team builds innovative products and platforms that support startup customers throughout their journey, from initial onboarding and credit programs to AI-powered guidance and scale solutions. Our portfolio serves hundreds of thousands of startup customers globally, and we partner with business development, field marketing, and solutions architecture teams worldwide. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising! The Amazon Sponsored Products and Brands is looking for a talented Sr. Applied Scientist to join the Global Optimization science team at the forefront of advertising technology innovation. We are working backwards from emerging markets to reimagine Amazon's advertising stack from the ground up, leveraging multilingual and multi-modal GenAI technologies — including semantic hash designs, LLMs, and more — to deliver solutions that scale to non-US marketplaces from day one. This is your opportunity to be part of a small, high-impact team that prototypes, invents, and ships state-of-the-art solutions that will define the future of digital advertising. The ideal candidate combines entrepreneurial drive with deep technical expertise, thriving in ambiguous, fast-moving environments where you define the path forward with minimal structure. Our team regularly goes from ideas on the whiteboard to production-grade experiments. Your Customer Obsession fuels your passion for translating science into real-world value for millions of advertisers and customers worldwide. You are consistently Right, A Lot when navigating complex technical decisions, demonstrating the rare ability to identify solutions that drive maximum impact with minimal resources. Whether you're architecting scalable GenAI systems or optimizing multi-task recommendation engines, you cut through complexity to find the solutions that deliver the most results. Your GenAI experience goes beyond theoretical knowledge—you have successfully delivered value with state-of-the-art technologies at Amazon scale, understanding both the promise and practical challenges of deploying machine learning in production environments (evaluations, etc.). You're not just a scientist or just an engineer—you're a builder who sees the bigger picture and knows how to make it reality. Key job responsibilities * Architect next-generation systems that replace legacy monolithic infrastructure with scalable, intelligent solutions that scale worldwide on day one. * Pioneer breakthrough applied science in multi-modal GenAI applications for advertising, combining text, image, and multi-lingual support. * Drive end-to-end innovation from research ideation through production deployment at Amazon scale. * Lead cross-functional collaboration with product, engineering, and business teams to translate science into customer impact. About the team You will join a newly-founded team with a broad mandate to experiment and innovate, with a focus on driving growth of sponsored products ad experiences across non-US marketplaces. This broad charter gives us the flexibility to explore and apply scientific techniques to novel product problems. You will have the satisfaction of seeing your work improve the experience of millions of Amazon shoppers worldwide while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills, and be a science leader in an environment that thrives on creativity, experimentation, and product innovation.

Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Hyderabad office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.

Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.

Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.

Amazon Lab126 builds the synthetic data that trains the AI in Amazon's devices, spanning wearables, smart home, and streaming. The team is looking for Applied Scientists to advance the science of making synthetic data maximally useful to the models it trains, across video, audio, and 3D. This is generation, but it is also the full loop around it: deciding what data to generate through data-centric methods like importance sampling and curriculum design, combining it with real data, and measuring its effect on the models that ship to customers. A large part of the opportunity lies in the physical environment and how it responds to activity around it, including fast-moving, rule-governed events where signals across modalities must stay aligned in space and time. You will develop generative and simulation methods, from diffusion and multimodal models to physics-based digital twins and sim-to-real transfer, grounded in precise 3D spatial understanding, and deliver science artifacts that feed the models shipping in Amazon's devices. This is a hands-on research role with technical lead scope, reporting to a manager within a cross-discipline organization of Applied Scientists and Software Development Engineers. Key job responsibilities - Develop and fine-tune generative models and 3D simulation pipelines across video, audio, and sensor modalities, advancing temporal coherence, identity preservation, controllability, and causality so that generation is action-conditioned and physically plausible. - Develop training recipes that combine synthetic and real data effectively, including mixing ratios, curriculum ordering, and sampling strategies, and characterize when synthetic data helps, when it hurts, and why. - Build evaluation methods that predict synthetic data's impact on downstream models without full training runs, using proxy signals and embedding-based diagnostics that shorten the feedback loop from days to minutes. - Model structured, rule-governed activity so that generated multimodal data stays coherent across modalities, 3D space, and time, and build 3D-consistent scene and event representations that keep generated data spatially faithful. - Validate all work by measured improvement on production models, ship science artifacts to products, and mentor junior scientists and interns. A day in the life You might start your morning reviewing experiment results from an overnight training run, then join a design discussion with engineers to plan how your model will be integrated into a production pipeline. After lunch, you could be prototyping a new approach to a problem your team recently identified, writing clean and well-documented code as you iterate. Later, you might pair with a teammate to review their research methodology or prepare findings for an upcoming science review. About the team Our team owns the synthetic data strategy that accelerates time to market across Amazon's devices business. Demand for this data is growing, and we are actively expanding into new product categories. Our goal is to own this strategy across multiple flagship product lines for the entire devices organization, dramatically reducing how long it takes to get new capabilities into customers' hands. We are a cross-discipline group of Applied Scientists and Software Development Engineers who partner closely to move ideas from research to production. If you want your science to directly shape the AI powering the next generation of Amazon devices, this is an exciting place to build your career.

We are seeking an Applied Scientist to join our AI Security team, which builds AI-based security analysis services that identify, validate, and help remediate vulnerabilities in Amazon source code and cloud configurations. The team combines large language models, security knowledge, code analysis, and security-engineer feedback to help builder teams detect security issues earlier in development and maintain security coverage across Amazon. As an Applied Scientist, you will research, model, design, and implement machine learning and agentic systems that reason over source code, infrastructure, and security context. You will collaborate with applied scientists, security engineers, software engineers, internal partners, and external researchers to improve finding precision, severity assessment, remediation quality, and continuous scanning coverage. Your work will enable continuous, AI-driven security analysis across Amazon’s software estate, converting source-code and infrastructure signals into validated, prioritized findings and proposed fixes that help builders prevent security defects from reaching production. Key job responsibilities • Research and develop accurate and scalable methods to solve our hardest AI security problems • Lead cross-functional work with applied scientists, software engineers, and security engineers to develop models and technical designs for AI systems that analyze source code, validate security findings, and generate remediation guidance at Amazon scale. About the team As an Applied Scientist on the AI Security team, you will research, develop, evaluate, and productionize models that identify security vulnerabilities in source code and cloud configurations. You will design experiments, build evaluation datasets and metrics, and apply large language models, program analysis, and security knowledge to improve finding precision, severity assessment, and remediation quality. You will work with applied scientists, security engineers, and software engineers to deliver systems that convert security signals into validated, prioritized findings and proposed fixes for builder teams across Amazon. Your work will shape how Amazon identifies and addresses security defects throughout the software development lifecycle. You will investigate model behavior, analyze failure modes, develop methods to improve recall and precision, and measure the impact of new capabilities before deployment. The role provides opportunities to solve applied machine learning problems across source code, infrastructure, cloud services, and security operations. About the team The AI Security team develops systems that analyze source code and cloud configurations to identify, validate, and help remediate security vulnerabilities. We combine large language models, security engineering, automated evaluation, and feedback from builders and security engineers to improve security coverage across Amazon. The team works on problems including vulnerability detection, severity assessment, false-positive reduction, remediation generation, continuous scanning, and security-data analysis. Diverse Experiences Amazon Security values varied professional and life experiences. We encourage candidates from traditional and nontraditional career paths, including candidates early in their careers and candidates with alternative technical backgrounds, to apply. Why Amazon Security? Security is central to customer trust and Amazon’s products and services. Our organization establishes and maintains security standards across Amazon. Team members build experience across cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture Amazon Security brings together people with different experiences, technical backgrounds, and perspectives. We invest in learning opportunities and create space for teams to examine security problems from multiple viewpoints. Training and Career Growth The team provides knowledge-sharing, technical training, mentorship, and opportunities to work across security domains. Applied Scientists can deepen their expertise in machine learning, security research, evaluation systems, and production AI services. Work/Life Balance We support work-life harmony and flexible working practices. Teams plan work with sustainable delivery in mind and support employees in meeting responsibilities at work and at home.

As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities Thought leader on the team and help set team directions Research multiple problem domains, suggest various approaches to try and be as hands-on as needed while providing more junior scientists with critical mentorship Collaborate with engineers to come up with the right LLD and HLD to solve key business problems Strong emphasis on communication via writing, internal and external talks, and being able to align with multiple stakeholders A day in the life As an Applied scientist II, a typical day will involve aligning with key product, engineering and business stakeholders ; advising junior scientists on the work they are doing ; reading current research papers and staying up-to-date on AI research ; diving deep as needed to improve CMT models and addressing stakeholders from the science perspective ; writing python code

As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities 1. Research the problem domain and come up with various approaches to solve the problem. 2. Be willing to experiment quickly and fail fast. 3. Collaborate with engineers to come up with the right end to end solution to the business problems. 4. Ideate on future roadmap for science in CMT 5. Be willing to roll up your sleeves and learn core topics outside applied science, for example ML engineering A day in the life A typical day might involve (a) working on ideas for improving models around product similarity or price recommendations, (b) working closely with other scientists and our ML engineers to ensure that the best models are in production, (c) writing good maintainable code that can be reused and reproduced, (d) sharing your work across CMT and beyond via technical writings and presentations

The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.