Careers

At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML 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 presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, 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.

ComPAS Business Insights sits at the intersection of pricing, marketing, and consumer science for Amazon's Companion Products & Services portfolio; spanning Accessories, Pre-Owned Business (POB), and Trade-In (TI). AI is fundamentally changing how we solve these problems. As a Sr. Data Scientist, you will drive that transformation: building advanced ML models and AI-powered tools that automate decision science at scale, turning complex pricing, targeting, and segmentation challenges into intelligent, self-improving systems. You will partner with product, marketing, finance, and engineering leaders to translate ambiguous problems into production-ready ML systems and AI-powered tools. Your work will span pricing science, consumer behavior analysis, marketing targeting, propensity score development, and customer segmentation - always with an eye toward how generative AI and foundation models can accelerate, scale, or reimagine the solution. Key job responsibilities Key Job Responsibilities • Own the full lifecycle of model development - from problem framing and exploratory analysis through feature engineering, model design, deployment, and continuous improvement. • Oversees the development of pricing science models, including price elasticity estimation, promotional effectiveness measurement, and optimal pricing recommendations across Accessories, POB, and TI product lines. • Build and refine propensity models and customer segmentation frameworks that enable precision marketing targeting and personalized customer engagement. • Conduct consumer behavior analysis to uncover purchase patterns, cross-sell opportunities, and drivers of performance across the ComPAS portfolio. • Leverage generative AI and LLMs (e.g., Amazon Bedrock, foundation models) to build intelligent tools that automate insights generation, scale analytical workflows, and solve problems that were previously intractable. • Identify and execute opportunities to optimize and automate existing analytical and scientific processes -ntransforming manual, repetitive work into scalable AI-powered pipelines. • Design and run rigorous experiments (A/B testing, causal inference, synthetic control) to measure impact and guide strategic decisions on pricing, marketing, and product. • Build data-driven business cases to prioritize science and AI initiatives, demonstrating measurable impact on revenue and customer outcomes. • Contribute to the broader science community by mentoring data scientists and publishing technical work in internal and external forums. A day in the life Your mornings start with decision science - framing a pricing or targeting problem, writing Python/SQL to prototype a model, or stress-testing a segmentation approach. Afternoons shift to AI tool-building: experimenting with foundation models, designing automation pipelines, or collaborating with engineers on deployment architecture. Between deep work blocks, you're leading problem-framing sessions with PMs, and business leaders, demoing AI prototypes to stakeholders, or hosting a Lunch & Learn that sparks the next automation idea across the team. About the team ComPAS Business Insights is the AI-first data science and analytics team powering Amazon's Companion Products & Services portfolio - Accessories, Pre-Owned Business (POB), and Trade-In (TI). We own the full stack: from production-grade data infrastructure and automated reporting to advanced decision science spanning pricing, consumer behavior, marketing targeting, segmentation, and propensity modeling. We are leveraging AI to build intelligent tools that automate workflows, democratize insights, and put self-service analytics at stakeholders' fingertips. Our mission: turn every pricing, marketing, and customer decision into a science-powered, AI-accelerated outcome.

Amazon Robotics is seeking an exceptional Applied Scientist to join our Foundation Models and Agentic AI team. This role presents an opportunity to shape the future of robotics through innovative applications of large vision-language and reasoning models, and reinforcement learning. At Amazon 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 - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through imitation learning and reinforcement 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. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.

What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI — at the same time. This role is a chance to shape the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale. We're looking for a Data Scientist who thrives at the intersection of applied ML, agentic AI, and security. You'll design and deploy models that detect threats, power intelligent agents, and make security decisions at cloud scale. You'll work shoulder-to-shoulder with SDEs, applied scientists, security researchers, and PMs on a team where the best idea wins, regardless of title or tenure. Key job responsibilities * Build the intelligence behind AI-first security products: Design, train, and ship ML models that power agentic systems, anomaly detection, threat classification, and automated response — all running across multi-cloud environments. * Own the full science lifecycle: From problem framing and data exploration through model development, evaluation, production deployment, and monitoring. You build it, you ship it, you run it. * Build with AI to build AI: Use agentic coding tools, LLM-powered workflows, and experimental AI tooling to accelerate every phase of your work; from EDA to feature engineering to model iteration. Multiply your velocity and raise the bar for what one scientist can deliver. * Power agentic architectures: Develop the models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready. * Prototype rapidly and validate with customers: Turn hypotheses into prototypes in days, not quarters. Iterate based on real customer signal and ship what works. * Partner across disciplines: Work directly with SDEs, applied scientists, security researchers, PMs, and UX designers to turn ambiguous problems into shipped solutions. Small team means short lines between you and the decision. * Communicate with impact: Translate complex modeling results into clear recommendations for engineers, product leaders, and senior executives. Influence direction with data. * Raise the science bar: Contribute to technical and science reviews, mentor teammates, and champion AI-first development practices. Help shape the science culture of a fast-growing team from the ground floor. A day in the life No two days look the same on this fast-growing, AI-first team. You might start your morning reviewing evaluation results from overnight model training runs, then dive into building a RAG pipeline or tuning a multi-agent orchestration loop. Before lunch, you're pair-prompting with an agentic coding assistant to stand up a new feature pipeline. In the afternoon, you join a design session with senior and principal scientists and engineers where your ideas carry weight regardless of title. You own science problems end to end, ship using the latest AI-assisted workflows, and see your models reach production fast. This is where builders thrive. About the team Amazon Enterprise Security Products is built by builders who tackle challenges others might consider too ambitious. We're a small team where there are no layers between you and the decision, no waiting quarters to see your work reach customers. Every team member brings an owner's mentality. If there's a problem worth solving, we solve it. No mission is beyond reach, no detail beneath our attention. We move fast, we ship fast, and we learn from what we ship. This is where builders who want to make the impossible routine come to do their best work. 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.

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.

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML 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 presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, 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.

Are you passionate about authorization, programming languages, applying formal verification, program analysis, constraint-solving, and/or theorem proving to real-world problems? Do you want to shape the future of an open-source authorization language that is becoming an industry standard? If so, then we have an exciting opportunity for you. Cedar is an open-source policy language and evaluation engine for authorization that is used across AWS services including Amazon Verified Permissions, AWS Systems Manager, and more. Cedar recently joined the Cloud Native Computing Foundation (CNCF) as a Sandbox project, and we are looking for an Applied Scientist to help advance Cedar's adoption, maturity, and community presence across the cloud-native ecosystem. In this role, you will drive the science and engineering behind Cedar's integration into cloud-native platforms such as Kubernetes, advance Cedar's formal verification and analysis capabilities, and serve as a technical leader and advocate within the CNCF community. You will interact with internal teams and external open-source communities to understand their authorization requirements, propose innovative solutions, create software prototypes, and productize prototypes into production systems. In addition, you will support and scale your solutions to meet the ever-growing demand of customer use. Key job responsibilities Technical Responsibilities - Drive the design and development of Cedar's integration into cloud-native authorization environments, including Kubernetes and other CNCF ecosystem projects. - Advance Cedar's formal verification, SMT-based analysis, and policy validation capabilities to raise the bar for authorization assurance. - Interact with various teams to develop an understanding of their security, authorization, and policy requirements. - Apply the acquired knowledge to build tools that find problems, or show the absence of security/safety problems, in authorization policies and systems. - Implement these tools through the use of SAT, SMT, and various concepts from programming languages, theorem proving, formal verification, and constraint solving. - Create software prototypes to verify and validate devised solutions; integrate prototypes into production systems using standard software development tools and methodologies. - Contribute to Cedar's open-source codebase as a maintainer, driving code quality, review standards, and technical direction. Leadership & Community Responsibilities - Represent Cedar and AWS at technical conferences, including CNCF events such as KubeCon, and advocate for Cedar adoption across the cloud-native community. - Can present and defend company-wide technical decisions to the internal technical community and represent the company effectively at technical conferences. - Functional thought leader, sought after for key tech decisions. Can successfully sell ideas to an executive-level decision maker. - Mentor and train the research scientist community on complex technical issues. - Collaborate with the open-source community to advance Cedar's CNCF project maturity (Sandbox → Incubation → Graduated). - Build and maintain relationships with cloud-native developers, contributors, and organizations to drive Cedar adoption and gather feedback. A day in the life You will be working on cutting-edge technology at the intersection of formal methods, automated reasoning, authorization, and cloud-native systems. You will collaborate with fellow applied scientists and engineers to solve challenging problems that provide value to customers by improving the security and usability of authorization. You will engage with the open-source community, contribute to Cedar's CNCF journey, and have an opportunity to publish your work and present at leading industry conferences. About the team The Cedar team builds and maintains Cedar, an open-source policy language and evaluation engine for authorization. Cedar is designed to be ergonomic, fast, and analyzable, backed by automated reasoning and formal verification. Cedar is used across multiple AWS services and has joined the CNCF as a Sandbox project, with the goal of becoming a Graduated project and an industry standard for authorization. The team works at the intersection of programming languages, formal methods, and cloud-native infrastructure.

The WW Operations IPAT team is revolutionizing Amazon’s financial forecasting through TrendCast, an innovative, automated, science-based top-down forecast modeling engine. As we expand our scope into Generative AI, we are building a sophisticated, LLM-powered Finance Knowledge Base to streamline decision-making. We are seeking a strong Data Scientist II to drive the technical strategy for these advanced analytical and AI-driven solutions. In this role, you will act as a technical lead, translating high-level business ambiguity into scalable, production-grade systems while influencing cross-functional roadmaps. Key job responsibilities • Own and solve difficult business problems where the solution approach is unclear, delivering high-quality artifacts that directly influence financial decisions for senior leadership • Apply a range of data science methodologies (statistical modeling, machine learning, time series analysis, econometrics) to solve complex forecasting challenges • Design and implement scalable, reliable approaches to extract insights from large, complex datasets across multiple domains • Develop metrics to quantify the benefits of solutions and measure project progress and success • Design and implement Retrieval-Augmented Generation (RAG) systems and LLM-based solutions to enhance financial knowledge retrieval and decision support • Proactively identify and solve challenges related to GenAI solutions including accuracy, latency, and context management • Partner with finance stakeholders, engineers, and other scientists to identify data requirements and deliver solutions that meet customer needs • Write clear, factually correct documents with substantial analytical components; explain technical concepts to non-technical audiences • Provide peer feedback on solutions and results; mentor and teach less experienced data scientists

AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for an Applied Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Applied Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases