Careers

Are you excited about building at the intersection of enterprise AI and customer experience? Are you passionate about applying science to real customer problems? Do you want your work shaped by real-world conditions and validated through customer deployments? The Alexa Enterprise team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong background in speech and audio machine learning who thrives in a startup-style environment. In this role, you'll set scientific direction and lead the development of techniques to improve speech-to-text accuracy for domain-specific use cases and optimize the performance and latency of end-to-end audio pipelines. You'll lead work across model evaluation and selection, fine-tuning, data and evaluation strategies, and real-time inference optimization. You'll partner closely with software engineers to turn scientific advances into production capabilities, while working with enterprise customers to understand how speech and audio systems perform in their environments. You'll turn improvements inspired by one customer's needs into capabilities that serve many, while influencing teams around a shared scientific vision. We're a small, fast-moving team building AI-powered solutions at the intersection of Alexa AI capabilities and AWS cloud services. Our customers span healthcare, energy, retail, and insurance, and they're deploying in environments where the technical challenges are real and the feedback loops are immediate. Key job responsibilities - Set the scientific direction for improving speech-to-text accuracy across domain-specific use cases and real-world operating conditions - Lead the evaluation, selection, adaptation, and fine-tuning of speech and audio models based on accuracy, latency, cost, reliability, and deployment constraints - Drive improvements to the performance and latency of end-to-end audio pipelines, from signal processing and streaming through inference and transcription - Define datasets, benchmarks, experiments, and evaluation methodologies that reflect customer use cases - Lead the optimization of models and inference pipelines for reliable, real-time operation - Work directly with enterprise customers to understand quality challenges, validate scientific improvements, and identify opportunities for broader platform capabilities - Turn patterns from customer engagements into reusable models, evaluation methods, and scientific capabilities that scale across customers and industries - Influence and collaborate with software engineering and partner teams to productionize models, measure their performance, and continuously improve deployed systems - Write scientific and technical documents, lead reviews, and communicate experimental results and trade-offs to engineering, product, and business leaders - Mentor Applied Scientists and engineers, participate in hiring, and raise the team's science and engineering standards About the team The Alexa Enterprise team builds AI-powered solutions for businesses. We work with enterprise customers across healthcare, energy, retail, and insurance to deploy solutions that transform operations. Our team operates at the intersection of Alexa AI capabilities and AWS cloud services, partnering closely with AWS sales, product, and specialist teams to deliver customer outcomes. Our work brings together applied AI, speech and audio science, real-time systems, and cloud services. Senior Applied Scientists on the team have real ownership, from identifying and framing customer problems through setting scientific direction, experimentation, production integration, and measurement of customer impact. This is an opportunity to lead meaningful scientific work while helping shape a platform in its early stages.

Are you excited about building at the intersection of enterprise AI and customer experience? Are you passionate about applying science to real customer problems? Do you want your work shaped by real-world conditions and validated through customer deployments? The Alexa Enterprise team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong background in speech and audio machine learning who thrives in a startup-style environment. In this role, you'll set scientific direction and lead the development of techniques to improve speech-to-text accuracy for domain-specific use cases and optimize the performance and latency of end-to-end audio pipelines. You'll lead work across model evaluation and selection, fine-tuning, data and evaluation strategies, and real-time inference optimization. You'll partner closely with software engineers to turn scientific advances into production capabilities, while working with enterprise customers to understand how speech and audio systems perform in their environments. You'll turn improvements inspired by one customer's needs into capabilities that serve many, while influencing teams around a shared scientific vision. We're a small, fast-moving team building AI-powered solutions at the intersection of Alexa AI capabilities and AWS cloud services. Our customers span healthcare, energy, retail, and insurance, and they're deploying in environments where the technical challenges are real and the feedback loops are immediate. Key job responsibilities - Set the scientific direction for improving speech-to-text accuracy across domain-specific use cases and real-world operating conditions - Lead the evaluation, selection, adaptation, and fine-tuning of speech and audio models based on accuracy, latency, cost, reliability, and deployment constraints - Drive improvements to the performance and latency of end-to-end audio pipelines, from signal processing and streaming through inference and transcription - Define datasets, benchmarks, experiments, and evaluation methodologies that reflect customer use cases - Lead the optimization of models and inference pipelines for reliable, real-time operation - Work directly with enterprise customers to understand quality challenges, validate scientific improvements, and identify opportunities for broader platform capabilities - Turn patterns from customer engagements into reusable models, evaluation methods, and scientific capabilities that scale across customers and industries - Influence and collaborate with software engineering and partner teams to productionize models, measure their performance, and continuously improve deployed systems - Write scientific and technical documents, lead reviews, and communicate experimental results and trade-offs to engineering, product, and business leaders - Mentor Applied Scientists and engineers, participate in hiring, and raise the team's science and engineering standards About the team The Alexa Enterprise team builds AI-powered solutions for businesses. We work with enterprise customers across healthcare, energy, retail, and insurance to deploy solutions that transform operations. Our team operates at the intersection of Alexa AI capabilities and AWS cloud services, partnering closely with AWS sales, product, and specialist teams to deliver customer outcomes. Our work brings together applied AI, speech and audio science, real-time systems, and cloud services. Senior Applied Scientists on the team have real ownership, from identifying and framing customer problems through setting scientific direction, experimentation, production integration, and measurement of customer impact. This is an opportunity to lead meaningful scientific work while helping shape a platform in its early stages.

Are you excited about building at the intersection of enterprise AI and customer experience? Are you passionate about applying science to real customer problems? Do you want your work shaped by real-world conditions and validated through customer deployments? The Alexa Enterprise team is looking for a passionate, talented, and inventive Applied Scientist with a strong background in speech and audio machine learning who thrives in a startup-style environment. In this role, you’ll develop and apply scientific techniques to improve speech-to-text accuracy for domain-specific use cases and optimize the performance and latency of end-to-end audio pipelines. You’ll work across model evaluation and selection, fine-tuning, data and evaluation strategies, and real-time inference optimization. You’ll partner closely with software engineers to turn scientific advances into production capabilities, while working with enterprise customers to understand how speech and audio systems perform in their environments. The improvements inspired by one customer’s needs become capabilities that serve many. We’re a small, fast-moving team building AI-powered solutions at the intersection of Alexa AI capabilities and AWS cloud services. Our customers span healthcare, energy, retail, and insurance, and they’re deploying in environments where the technical challenges are real and the feedback loops are immediate. Key job responsibilities - Develop and apply modeling techniques to improve speech-to-text accuracy for domain-specific use cases and real-world operating conditions - Evaluate, select, adapt, and fine-tune speech and audio models based on accuracy, latency, cost, reliability, and deployment constraints - Improve the performance and latency of end-to-end audio pipelines, from signal processing and streaming through inference and transcription - Design datasets, benchmarks, experiments, and evaluation methodologies that reflect customer use cases - Optimize models and inference pipelines for reliable, real-time operation - Work directly with enterprise customers to understand quality challenges, validate scientific improvements, and identify opportunities for broader platform capabilities - Turn patterns from customer engagements into reusable models, evaluation methods, and scientific capabilities that scale across customers and industries - Collaborate with software engineers to productionize models, measure their performance, and continuously improve deployed systems - Write scientific and technical documents, lead reviews, and communicate experimental results and trade-offs to engineering, product, and business stakeholders - Contribute to the team’s scientific direction, mentor teammates, participate in hiring, and improve science and engineering processes About the team The Alexa Enterprise team builds AI-powered solutions for businesses. We work with enterprise customers across healthcare, energy, retail, and insurance to deploy solutions that transform operations. Our team operates at the intersection of Alexa AI capabilities and AWS cloud services, partnering closely with AWS sales, product, and specialist teams to deliver customer outcomes. Our work brings together applied AI, speech and audio science, real-time systems, and cloud services. Applied Scientists on the team have real ownership, from identifying and framing customer problems through experimentation, production integration, and measurement of customer impact. This is an opportunity to solve meaningful scientific problems while helping shape a platform in its early stages.

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 looking for a Senior Applied Scientist to build the science that helps Amazon's advertisers grow — with a focus on Cross Border Sellers who face distinct barriers as they scale across multiple marketplaces, and this role is about understanding those pain points deeply and removing them: building intelligent, autonomous solutions that simplify advertising, act efficiently on the advertiser's behalf, and let advertisers accelerate their growth and success. We expect a Senior Scientist to think innovatively about how to reduce the effort and complexity of advertising for these advertisers. Working backwards from their needs — spanning hands-off sellers and global brands with cross-marketplace operations — you will take the lead on medium-to-large, ambiguous problems where neither the problem nor the solution is well defined, invent new methods, validate them through rigorous experimentation, and deliver customer-facing products with measurable business impact. This role combines science depth, product focus, and hands-on engineering: you will raise the science bar, build consensus on approach across product and engineering partners, and mentor scientists and engineers while remaining deeply hands-on with the hardest technical problems. Key job responsibilities As a Senior Applied Scientist on this team you will: - Understand the pain points of Cross border advertisers as they scale across marketplaces, and build science-driven solutions that remove barriers and accelerate their growth and success. - Build agentic and ML systems that autonomously create, structure, and manage ad campaigns on advertisers' behalf, encoding auction and marketplace dynamics (bidding, budget pacing, targeting decisions) while balancing advertiser ROI, shopper experience, and marketplace health. - Innovate on new, simpler ways to advertise powered by GenAI, and push the frontier of existing autonomous programs (e.g., auto-targeting, global lift-and-shift) while proposing and prototyping the next generation of campaign automation. - Develop models across the campaign lifecycle — opportunity discovery, ranking, ad-readiness and demand prediction, and bid/budget optimization — and apply the right approach for each problem, from classical ML to LLM/reasoning methods. - Define and curate the datasets and signals needed to train and evaluate these systems — advertiser and campaign data, cross-marketplace performance, auction and bid/budget signals, impressions, clicks, conversions, and search-term/keyword performance. - Own core parts of the agentic architecture — planning, tool use and integration (e.g., MCP), reasoning frameworks (e.g., ReAct, CoT/ToT), and model customization — and define evaluation and safety methodology so that automated decisions are reliable and trustworthy. - Stay deeply hands-on: write production-quality, critical-path code and build core components that take systems from prototype to launch on large-scale pipelines (Spark/EMR, Airflow) and online serving. - Raise the science bar: mentor scientists and engineers, review designs and experiment plans, and communicate results and tradeoffs clearly to technical and business leaders. About the team Autonomous SP drives growth and simplifies advertising for Amazon's hands-off advertisers by creating and enhancing autonomous campaign solutions. We lead existing successful programs, including auto-targeting and global lift-and-shift, and continually innovate by building new, simpler campaign constructs powered by GenAI to act efficiently on advertisers' behalf. Cross-border Seller Experience (CBSX) focuses on developing targeted solutions for domestic and global advertisers with multi-marketplace operations. By understanding and addressing their unique Seller Central needs, we optimize for cross-marketplace efficiency, improved performance, and streamlined advertising experiences. Our team operates horizontally, delivering impactful solutions that benefit both hands-on and hands-off advertisers globally. Together, we sit within Sponsored Products and Brands, which is re-imagining advertising through the latest generative AI — building responsible, intelligent systems that balance the needs of advertisers, the shopping experience, and marketplace health.

Amazon is looking for an Applied Scientist with a strong deep learning background to help build the training environments that teach large language models to reason, use tools, and complete complex, multi-step tasks. Key job responsibilities You will design, build, and evaluate reinforcement learning environments (RL gyms) for LLM training: realistic, verifiable task environments where models learn through interaction and feedback. This includes defining tasks and reward signals, building the infrastructure to run environments at scale, measuring how well environments transfer into model capability, and developing rigorous methods to verify environment quality and correctness. You will work closely with model training teams to understand where environments are most valuable and iterate based on what the models learn. Your work will directly shape products and services that use generative AI. About the team The Foundational AI (FAI) team has a mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As an Applied Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning, and advanced clustering. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Invent and adapt new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Design and deploy production-quality ML components that directly impact advertiser trust and the business top-line. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Contribute to the scientific community through publications at peer-reviewed venues and reviewing research submissions. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)

SCOT's RoW Regional team is seeking an experienced and motivated Data Scientist to help optimize, enhance, and scale Supply Chain decision algorithms in countries beyond US/EU. The focus will be on enhancing and also building new solutions solving for forecasting, buying, placement and inventory health management issues in 10 stores. You will work closely with Product Managers Tech, BIEs, SDEs, Supply Chain Managers, and other Scientists across US and EU to build scalable, high insight- high impact products and own improvements to business outcomes within your area, enabling execution of WW and local solutions for next generation Supply Chain systems. You will leverage your expertise in modeling, maths, algorithms and more importantly AI to analyze diverse and large datasets to uncover insights and build working models that help minimize dependence on manual supply chain decisions within the vast landscape and selection in which Amazon operates. Areas of work would include: 1) Building OR, RL, Agentic AI based models for automated supply chain optimization decisions; integrating with the central tech solutions owned by SCOT WW, 2) Creating decision frameworks to choose between multiple models to solve for a given supply chain problem, 3) Build visibility, audit and decision frameworks that help operate complex and nuanced supply chains across 10 countries. Key job responsibilities You will be responsible for researching, experimenting and analyzing predictive and optimization models. You will work on ambiguous and complex business and research science problems with large opportunities. You are an analytical individual who is comfortable working with cross-functional teams and systems. Significantly AI Savvy. You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with product managers to understand their business requirements and operational processes. - Frame business problems into scalable solutions. - Adapt existing and invent new techniques for solutions. - Be the proponent of using latest techniques in data science and leveraging AI to fasten experimentation and model launch. - Gather data required for analysis and model building. - Collaborate with product and engineering teams to design rigorous experiments to evaluate the impact of new features or algorithms. - Create and track accuracy and performance metrics. - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation. - Represent the team and contribute to science forums within Amazon About the team Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, Amazon’s Supply Chain Optimization Technology (SCOT) organization is for you. At SCOT, we solve deep technical problems and build innovative solutions in a fast-paced environment working with smart & passionate team members. (Learn more about SCOT: http://bit.ly/amazon-scot)

AWS Applied AI Solutions (AAIS) is where science meets customer obsession at scale. We build the intelligent systems that power AWS services used by millions, combining research in machine learning, agentic AI, and applied science with the operational rigor required to deliver enterprise grade experiences. Within AAIS, Amazon WorkSpaces is our cloud based virtual desktop service that delivers secure, managed computing to over one million daily users across the globe, enabling organizations to provision, manage, and scale desktops with the reliability and performance their workforce depends on. We are looking for a Senior Applied Scientist to own and advance the science behind capacity modelling for Amazon WorkSpaces. You will design, build, and continuously improve the forecasting and optimization models that ensure the right compute, storage, and networking resources are available at the right time, in the right regions, at the lowest possible cost, without ever compromising the end user experience. This is a high impact individual contributor role for someone who thrives at the intersection of applied research and production systems. You will define the scientific roadmap for capacity intelligence, turning reactive provisioning into a predictive, self optimizing engine that anticipates demand before customers feel any constraint. Key job responsibilities Define and drive the scientific strategy for capacity modelling, establishing the research agenda that transforms how WorkSpaces forecasts demand, plans supply, and allocates resources across a globally distributed infrastructure. Build advanced demand forecasting models that predict workspace usage across multiple time horizons, from intraday spikes to long range growth trajectories, incorporating signals such as customer onboarding patterns, seasonal trends, regional expansion, and macroeconomic indicators. Design supply optimization frameworks that determine optimal resource placement, instance mix, and pre warming strategies, balancing availability, performance, and cost by reasoning over hardware constraints, pricing dynamics, and service level objectives. Develop causal and probabilistic models that move beyond trend extrapolation to true understanding of demand drivers, enabling the organization to distinguish organic growth from one time events, anticipate shifts in usage patterns, and quantify uncertainty in planning decisions. Architect simulation and scenario planning systems that allow business and engineering leaders to run what if analyses, stress test capacity plans against disruption scenarios, and evaluate trade offs between investment timing, risk tolerance, and customer experience. Pioneer the integration of machine learning with operations research, combining deep learning based forecasting with mathematical optimization to jointly solve the demand prediction and resource allocation problem in a way that neither discipline can achieve alone. Establish evaluation frameworks and monitoring systems that measure forecast accuracy, capacity utilization, and cost efficiency in production, creating tight feedback loops that drive continuous model improvement and build organizational trust in science driven planning. Influence the broader organization's capacity strategy by translating model outputs into actionable recommendations for leadership, identifying opportunities to extend capacity intelligence patterns to adjacent services, and mentoring scientists and engineers across the team.

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 Amazonwe 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 - Collaborate with simulation and robotics experts to translate physical modeling needs into robust, scalable, and maintainable simulation solutions. - Design and implement high-performance simulation modeling and tools for rigid and deformable body simulation. - Identify and optimize performance bottlenecks in simulation pipelines to support real-time and batch simulation workflows. - Help build validation and unit testing pipelines to ensure correctness and physical fidelity of simulation results. - Identify potential sources of sim-to-real gaps and propose modeling and numerical approximations to reduce them. - Stay current with the latest advances in numerical methods, parallel computing, and GPU architectures, and incorporate them into our tools.

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 unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic 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 Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: Enable unprecedented generalization across diverse tasks Enable unprecedented robustness and reliability, industry-ready Integrate multi-modal learning capabilities (visual, tactile, linguistic) Accelerate skill acquisition through demonstration learning Enhance robotic perception and environmental understanding Streamline development processes through reusable capabilities 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. As an Applied Science Manager in the Foundation Model team, you will build and lead a team that develops and improves machine learning systems that help robots perceive, reason, and act in real-world environments. You will set the technical direction for leveraging state-of-the-art models (open source and internal research), evaluating them on representative tasks, and adapting/optimizing them to meet robustness, safety, and performance needs. You will drive the capability roadmap and the evaluation strategy that defines “what the robot brain can do,” and you will sponsor targeted innovation when gaps remain. You’ll collaborate closely with research, controls, hardware, and product teams, and ensure the team’s outputs can be further customized and deployed by downstream teams on specific robot embodiments.