Careers

Alexa is looking for an Applied Scientist with a strong background in Natural Language Processing (NLP) and Large Language Models (LLMs) to help build state-of-the-art conversational systems. In this role, you will collaborate with a large team of scientists training the Large Language Models that power the Alexa stack, as well as software engineers serving them in production systems. You will own solutions end-to-end: from ideation and research through to production deployment, enabling conversational assistants to support external tools, leverage diverse sources of information, and deliver novel reasoning capabilities to millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants. You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyze customer behaviors and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications. You would be able to bi-modal on science and engineering: someone who combines strong scientific foundations with the execution skills to ship high-quality solutions. A day in the life As an Applied Scientist on the Alexa Science team, you'll drive innovation in evaluating new product experiences while discovering novel approaches to enhance model capabilities and enrich customer interactions. You'll collaborate with cross-functional teams of engineers and scientists to identify root causes of model and system integration issues, continuously improving the end-to-end customer experience. You'll partner closely with scientists developing and fine-tuning large language models, engineers building low-latency inference infrastructure, and product teams defining customer experience metrics. About the team We are a team of applied scientists and engineers building the intelligence layer that powers Alexa+. Our work sits at the intersection of large language models, decision-making under uncertainty, and production ML systems. What we build directly shapes the customer experience: determining which models serve their requests, optimizing response latency, and creating natural, seamless interactions. We're a collaborative team that values rigorous experimentation, clear communication, and delivering solutions that perform at scale in real-world environments.

Amazon AGI Lab is a frontier research and product team combining the speed of a startup with Amazon’s scale and resources. We build agents that can perceive, reason, and take action to complete real-world tasks. The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We're hiring a principal engineer who can take models from prototype to production and build the systems that make them run reliably at scale. The bar is end-to-end ownership: your work can range from working alongside researchers to build novel architectures, to being the person who decides what the agent runtime looks like, where the data lives, and how we know it's delivering value. Key job responsibilities - Set the technical direction for the team - Partner closely with researchers to take emerging VLM and agent ideas from prototype to robust, instrumented systems that can be evaluated, improved, and scaled - Create tooling that accelerates research and engineering velocity - Raise the engineering bar for the team through technical design reviews, mentoring, principled architecture, high-quality code, observability, and operational excellence - Influence the broader AGI organization by identifying reusable primitives, writing clear technical strategy, and creating systems that other teams can build on - Be a thought leader & represent the lab externally by sharing ideas through thoughtful writing, conference talks, research publications, and open-source contributions, helping advance the field while raising the visibility and impact of the team’s work

The Amazon Devices & Services Demand Science (DSci) team is seeking a scientist with strong and AI/ML and communication skills to help with demand forecasting and supply optimization for the entire Amazon device family of products, services, and accessories. Amazon Devices represents a highly complex space with 100+ products across several product categories (e-readers [Kindle], tablets [Fire Tablets], TV [Fire TV and remote], smart speakers and audio assistants [Echo], wifi routers [eero], and video doorbells and cameras [Ring and Blink]), for sales both online and in offline retailers globally. We develop scalable and robust state-of-the-art ML, AI, and automation solutions — including transformer-based forecasting architectures, large language model (LLM)-powered agents, and agentic AI workflows — that learn from diverse data sources and power advanced predictive models. With better forecasts we drive down supply chain costs, enabling the offer of lower prices and better in-stock selection for our customers. In this role, you will own the full lifecycle — from research through production deployment. You will drive end-to-end solutions: understanding business requirements, exploring large-scale historical data and ML models, building prototypes, developing conceptually new approaches (including AI-native experimentation and agent-driven automation), and partnering with engineering teams to deploy and maintain solutions in production. You will collaborate closely with scientists, engineering peers, and business stakeholders. You will be at the heart of a growing and exciting focus area for Amazon Devices and Services where AI is transforming how we forecast, validate, and optimize decisions. You are an individual with strong science abilities, excellent communication skills, solid coding skills, and comfort with modern AI tools and frameworks. You will be responsible for researching, prototyping, experimenting, analyzing predictive models, implementing production-ready solutions, and developing AI-driven automation that progressively reduces manual intervention and enables hands-off-the-wheel science operations. Key job responsibilities - Build forecasting models from prototype through production, working closely with engineering to deploy at scale - Find and integrate new data sources to improve forecast accuracy and coverage - Design and deliver production-ready solutions for business-critical forecasting and optimization problems - Define and track performance metrics — both technical (error rates, bias, coverage) and business (plan attainment, financial impact, reduction in manual overrides) - Write and maintain clear technical documentation; present findings and recommendations to scientists, engineers, and business leaders - Set team standards for methodology, code quality, experimentation rigor, and AI-assisted workflows About the team We are a focused science team looking for help with enhancing the demand forecasting framework we're building and figuring out how to best solve the needs of both our internal stakeholders and cross-functional partners.

Amazon's Pricing Science is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon products worldwide. We are looking for a talented, organized, and customer-focused applied researchers to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our pricing algorithms across all products listed on Amazon. This role requires an individual with exceptional machine learning and predictive modeling skills, causal and experimental evaluation experience, excellent cross-functional collaboration skills and 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 independently to deliver business impact. Key job responsibilities - See the big picture. Understand and develop science to influence the long term vision for Amazon's science-based competitive, perception-preserving pricing techniques - Build strong collaborations. Partner with product, engineering, and data teams within Pricing & Promotions to deploy models at Amazon scale - Stay informed. Establish mechanisms to stay up to date on latest scientific advancements in machine learning, reinforcement learning, causal ML, 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. - Successfully execute & deliver. Apply your exceptional technical machine learning expertise to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring an applied scientist to drive our pricing optimization initiatives. The Price Optimization science team drives cross-domain and cross-system improvements through: - Invent and deliver price optimization, simulation, and competitiveness tools for Sellers. - Promotion optimization initiatives exploring CX, discount amount, and cross-product optimization opportunities. - Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into many partner-team architectures, and is highly relevant to the customer, therefore 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 About the team: the Pricing Optimization team within P2 Science owns price quality, discovery and discount optimization initiatives, including criteria for internal price matching, price discovery into search, p13N and SP, pricing bandits, and Promotion type optimization. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through 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. We are seeking a technical leader for our Supply Science team. This team is within the Sponsored Product team, and works on complex engineering, optimization, econometric, and user-experience problems in order to deliver relevant product ads on Amazon search and detail pages world-wide. The team operates with the dual objective of enhancing the experience of Amazon shoppers and enabling the monetization of our online and mobile page properties. Our work spans ML and Data science across predictive modeling, reinforcement learning (Bandits), adaptive experimentation, causal inference, data engineering. Key job responsibilities Search Supply and Experiences, within Sponsored Products, is seeking a Senior Applied Scientist to join a fast growing team with the mandate of creating new ads experience that elevates the shopping experience for our hundreds of millions customers worldwide. We are looking for a top analytical mind capable of understanding our complex ecosystem of advertisers participating in a pay-per-click model– and leveraging this knowledge to help turn the flywheel of the business. As a Senior Applied Scientist on this team you will: --Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects. --Lead technical efforts within this team and across other teams. --Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. --Run A/B experiments, gather data, and perform statistical analysis. --Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. --Work closely with software engineers to assist in productionizing your ML models. --Research new machine learning approaches. --Recruit Applied Scientists to the team and act as a mentor to other scientists on the team. A day in the life The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and with an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the direction is a must. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to customers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon

The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide fee strategy, quantify its impact, and ensure fees are accurately computed and explained for billions of transactions between Amazon selling partners and customers. We help build the foundations for growing selling partner businesses, bringing the best selection and prices to Amazon customers, and helping Amazon leaders make and implement high impact decisions that optimally balance profitability and growth. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact. As an data scientist on our team, this role will focus on the application of data analysis, econometrics, machine learning, and artificial intelligence to measure and predict Amazon's P&L, with emphasis on fee revenue. This blends the tools of data science, statistics, and ML/AI. Your work will shape not only how fees are decided, but how they are interpreted and planned. We are seeking scientists who are motivated by first principles, disciplined experimentation, and the technical challenge of deploying ideas at global scale. This is an opportunity to work on consequential problems where analytic rigor meets real-world complexity, and where your analysis, models, algorithms, and systems will directly influence the experience of millions of sellers. If you are driven to build elegant solutions to hard problems—and to see them operate in production at meaningful scale—we would welcome the opportunity to build with you. Key job responsibilities ** Translate ambiguous business challenges into well-defined scientific problems with measurable impact. ** Identify opportunities to improve fee revenue measurement, prediction, planning, structure, and level. ** Identify opportunities to improve measurement, and prediction of other items of the P&L, at appropriate levels of granularity. ** Design, develop, and deploy econometric or AI/ML models that improve our understanding of the relationship between fees and costs, or predict fee revenue, and other elements of the P&L. ** Partner closely with finance and fee strategy teams to formulate scientific questions, communicate results, and productionalize solutions. **Apply rigorous simulation methods to validate models and quantify business impact at scale. **Communicate scientific innovations and results clearly to cross-functional stakeholders and contribute to the broader internal and external scientific community through publications, talks, and technical artifacts. About the team Amazon’s third-party marketplace is a multibillion-dollar global service, connecting customers and sellers across through billions of transactions annually. The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide business fee strategy, ensure fees are accurately computed for millions of products, and improve the seller experience with AI tools that support any fee related contact (understanding, audit, and dispute). We build the scientific foundation that empowers sellers to grow their businesses with clarity and confidence. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact.

As part of the AWS Applied AI Solutions organization, we're building the future of AI-powered enterprise services across multiple domains. Our vision is to be the trusted foundation for transforming every business with Amazon AI teammates. Our mission is to deliver turnkey, enterprise-grade foundational AI capabilities that create delightful AI powered solutions. We're developing sophisticated AI systems that address complex challenges across autonomous operations, geospatial intelligence, trust and safety, IoT services, and foundational AI platforms. Key job responsibilities * Develop and productize AI solutions that address complex technical challenges requiring novel approaches beyond off-the-shelf tools * Design and implement machine learning systems for diverse applications including video understanding, geospatial optimization, fraud detection, anomaly detection, and automation * Create scalable algorithms and models that generalize across multiple customer use cases and business problems * Conduct rigorous experimentation with state-of-the-art techniques including large language models, computer vision, federated learning, or physics-based modeling, and agentic AI systems * Collaborate with engineering teams to integrate science components into production systems with measurable customer impact * Work directly with product teams to understand requirements, frame ambiguous problems into tractable science solutions, and validate approaches through proof of concepts * Establish evaluation frameworks and best practices for measuring solution performance and business impact * Mentor other scientists and contribute to the broader scientific community through publications when appropriate A day in the life As an Applied Scientist, you'll work on challenging problems that span multiple domains within AWS Core Services. You might develop video processing architectures for autonomous systems, create optimization solvers for geospatial applications, build behavioral detection models for fraud prevention, design anomaly detection systems for IoT devices, or develop specialized AI capabilities for platform services. You'll investigate novel approaches, validate ideas through rigorous experimentation with real data, and collaborate with scientists and engineers to transform research insights into scalable solutions. About the team Our team is a central science organization supporting multiple product teams across AWS Core Services. We tackle fundamental challenges in AI and machine learning that require novel approaches beyond off-the-shelf solutions. Working at the intersection of machine learning, large language models, and domain-specific applications, we develop practical techniques that advance the state-of-the-art while maintaining a clear path to customer impact. Our team builds deep domain expertise across geospatial intelligence, trust and safety systems, autonomous operations, and other critical areas, collaborating closely with engineering teams to transform research insights into scalable production solutions.

We are seeking an Applied Scientist to join the Amazon Precision Match (APM) team within Customer Journey, Network Solutions. APM is a transformative initiative replacing Amazon's legacy queue-based customer service routing with intelligent algorithmic matching — connecting customers with the best available service option based on their needs and Customer Service Associates (CSA) capabilities. This role will drive the science behind a high-scale system with significant projected impact on operational efficiency and customer experience. You will work at the intersection of recommendation systems, real-time ML inference, and large-scale experimentation to redefine how Amazon serves its customers. Key job responsibilities - Design, develop, and optimize ML-based matching algorithms that pair customers with optimal CSAs based on contact complexity, intent, and CSA skill profiles. - Build and iterate on feature engineering pipelines across CSA-level (skills, tenure, sentiment handling), contact-level (intent, complexity, urgency), and customer-level (language, communication style) attributes. - Run offline simulations on large-scale historical contact data and design statistically rigorous A/B experiments to validate matching improvements. - Develop real-time low-latency scoring and inference systems for production contact routing. - Address the cold start problem for new CSAs and build continuous model retraining infrastructure using production feedback. - Partner with CS Economics, Capacity Planning, and Quality teams on experiment design and results interpretation. - Evolve the matching framework from individual CSA ranking to set-based optimization balancing performance and operational sustainability. A day in the life You will spend your days iterating on matching models, analyzing experiment results from live production traffic, and collaborating with engineers and product managers to translate science insights into system improvements. You'll partner with the Customer Service Economics team to design experiments, review simulation outputs, and present findings to senior leadership. You'll also deep-dive into CSA behavioral patterns, contact transcripts, and performance data to identify new matching signals and continuously improve the algorithm. About the team The Amazon Precision Match team is a high-impact, fast-moving science and engineering team within Customer Journey, Network Solutions. Our mission is to ensure every Amazon customer is connected with the right service option at the right time — improving customer experience while driving operational efficiency at scale. We value intellectual curiosity, rigorous experimentation, and a bias for action. We operate with a continuous improvement flywheel: offline simulation, A/B testing, and production rollout. We collaborate closely with Customer Service Operations, Capacity Planning, Quality, and partner science teams across Amazon.

Applied Scientists in AWS Science of Security are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty. Key job responsibilities The successful candidate will: * Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. * Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. *Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. * Develop strategic plans to identify fundamentally new solutions for business problems. * Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team 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.

Our team is involved with pre-silicon design verification for custom IP. A critical requirement of the verification flow is the requirement of legal and realistic stimulus of a custom Machine Learning Accelerator Chip. Content creation is built using formal methods that model legal behavior of the design and then solving the problem to create the specific assembly tests. The entire frame work for creating these custom tests is developed using a SMT solver and custom software code to guide the solution space into templated scenarios. This highly visible and innovative role requires the design of this solving framework and collaborating with design verification engineers, hardware architects and designers to ensure that interesting content can be created for the projects needs. Key job responsibilities Develop an understanding for a custom machine learning instruction set architecture. Model correctness of instruction streams using first order logic. Create custom API's to allow control over scheduling and randomness. Deploy algorithms to ensure concurrent code is safely constructed. Create coverage metrics to ensure solution space coverage. Use novel methods like machine learning to automate content creation. About the team Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for customers who require specialized security solutions for their cloud services. Annapurna Labs (our organization within AWS UC) designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago—even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world.