Careers

Are you passionate about using science to make the digital world more secure? The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is looking for an Applied Scientist III to invent and build ML-driven systems that fundamentally change how Amazon identifies, prioritizes, and mitigates application security risk at scale. Our team sits at the intersection of data science, machine learning, and security operations. We build the intelligence layer that powers Amazon's application security programs: risk-scoring models that rank tens of thousands of applications, graph-based systems that map security context across architectures, and analytics platforms that drive data-informed decisions for security leadership. This is science with direct, measurable impact on Amazon's security posture. As an Applied Scientist III, you will lead the invention and delivery of novel ML solutions for complex, ambiguous problems in the security domain. You will work with large-scale datasets spanning application metadata, code signals, vulnerability findings, and organizational context to develop models that help Amazon focus security resources where they matter most. Key job responsibilities - Lead the design, development, and deployment of ML models and scientific solutions for application security prioritization, complexity scoring, and risk assessment - Frame ambiguous security problems into well-defined scientific challenges, proposing novel approaches when existing methodologies are insufficient - Architect and implement production-grade ML pipelines (feature extraction, model training, scoring, deployment) on AWS services (S3, Glue, SageMaker, Neptune) - Develop and extend graph-based models that capture security-relevant relationships between applications, services, teams, and vulnerabilities - Drive the team's scientific agenda by proposing new research initiatives, conducting experiments, and iterating on models using rigorous evaluation methodologies - Partner with security engineers, data engineers, and TPMs to translate model outputs into actionable intelligence for security review programs - Establish and raise the bar for scientific rigor: peer review code and designs, set best practices for experimentation, and document findings for reproducibility - Publish results internally and externally at peer-reviewed venues when appropriate About the team The Data Analytics & Engineering (DNA) team is a small, high-impact group within Amazon's Application Security organization. We build ML models, graph-based systems, and analytics platforms that determine how Amazon prioritizes security coverage across tens of thousands of applications. We're a hybrid team of scientists, data engineers, and security engineers who ship production science, embrace ambiguity, and operate with high ownership. If you want meaningful work that protects customers at Amazon's scale, this is 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.

As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. We are looking for a Senior Applied Scientist to join our team that is building revolutionary enterprise applications leveraging machine learning, generative AI, and agentic AI to help millions of companies worldwide manage their day-to-day supply chain operations. Our mission is to accelerate our customers' businesses through intuitive, differentiated technology solutions that solve enduring supply chain challenges. We blend strategic vision with curiosity and Amazon's real-world operational experience to build opinionated, turnkey solutions that make the 'buy versus build' decision a no-brainer for our customers. As a Senior Applied Scientist, you will design and develop state-of-the-art machine learning models and algorithms that power intelligent supply chain applications at global scale. You will work at the intersection of research and real-world product impact—translating scientific breakthroughs into production systems that serve millions of customers. We operate like a startup within AWS, offering you the opportunity to tackle unprecedented challenges while working with the latest technologies in deep learning, large language models, and optimization. If you are passionate about pushing the boundaries of applied science, thrive in ambiguous problem spaces, and want to shape the future of supply chain intelligence while having the backing of AWS's extensive resources, we want to hear from you. Key job responsibilities • Design, develop, and deploy novel machine learning models for demand forecasting, inventory optimization, anomaly detection, and supply chain decision-making. • Lead the development of GenAI and Agentic AI solutions that automate complex supply chain workflows and deliver intelligent, adaptive recommendations to customers. • Formulate real-world business problems as machine learning problems; define data requirements, model architectures, evaluation metrics, and experimentation frameworks. • Drive end-to-end applied science projects from ideation through experimentation, offline evaluation, A/B testing, and production deployment at scale. • Publish research findings in top-tier conferences (NeurIPS, ICML, KDD, AAAI) and file patents to advance Amazon's intellectual property. • Mentor and develop junior scientists; raise the technical bar for the science team through code reviews, design reviews, and knowledge sharing. • Collaborate closely with engineering, product management, and business stakeholders to translate scientific capabilities into customer-facing product features. • Influence the technical strategy and scientific roadmap for the organization; identify new areas of investment and emerging opportunities in AI/ML. • Establish and promote best practices for experimentation, model validation, and responsible AI development across the team. About the team The AWS Applied AI Solutions team builds enterprise applications that leverage Amazon's operational expertise to solve real-world supply chain challenges for millions of companies. We operate like a startup within AWS—moving fast, shipping iteratively using state-of-the-art AI technologies. We invest in your growth through mentorship from senior scientists, conference publication support, and internal science reading groups. Amazon values diverse experiences—even if you don't meet all preferred qualifications, we encourage you to apply. If your career hasn't followed a traditional path, don't let that stop you. ABOUT AWS: Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. 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 in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better? Amazon Robotics is looking for a talented Applied Scientist. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations. Key job responsibilities In this role you will: • Work closely with other scientists and engineers, and be part of Amazon’s diverse global science community. • Publish your research in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise. A day in the life You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale. About the team The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.

Join our Frontier AI & Robotics team to support the development of test infrastructure for next-generation robotic systems that will transform how robots perceive and interact with the world. You'll take ownership of designing and implementing software-driven test and validation frameworks across advanced actuators, precision sensors, and robotic subsystems — ensuring engineering validation and manufacturing test readiness to support breakthrough AI research and real-world deployment. Key job responsibilities - Test Infrastructure Development - Design, develop, and maintain automated test frameworks for engineering validation and manufacturing test of robotic systems and subsystems. Build scalable, reusable test software that integrates with hardware-in-the-loop (HIL) environments, data acquisition systems, and robotic control interfaces. - Software Integration & Automation - Develop test automation software in Python, C++, or equivalent languages to exercise actuators, sensors, vision systems, and communication interfaces. Implement scripted test sequences, data logging pipelines, and pass/fail criteria for prototype and production-intent hardware. - Hardware Bring-Up & SW/HW Integration - Perform hands-on hardware bring-up of robotic subsystems including power sequencing, communication interface validation, peripheral initialization, and firmware/software integration. Debug across the full stack, from board-level hardware through embedded firmware to application-layer test software, to bring new hardware revisions to a validated, testable state. - Engineering Validation & Mfg Test - Create and execute test protocols for functional validation, performance characterization, and regression testing of robotic subsystems. Develop test stations and software tooling that transition from R&D validation through manufacturing test readiness. - Debugging & Failure Analysis - Troubleshoot and root-cause issues across the robotic platform (power, compute, comms, actuators, sensors) using software diagnostic tools, log analysis, and bench instrumentation. Conduct failure analysis from component to system level. Reproduce critical failures and bridge communication between the lab and engineering teams. - Data Analysis & Reporting - Build data pipelines and analysis tools to aggregate test results, identify trends, and generate automated test reports. Develop dashboards or visualization tools that provide engineering teams with actionable insights on hardware quality and reliability. - Technical Documentation - Author and maintain test plans, test procedures, automation framework documentation, failure analysis reports, and troubleshooting guides; uphold consistent documentation standards across the lab. - Lab Operations Support - Support equipment maintenance, inventory management, vendor coordination, and safety/regulatory compliance for test lab environments. A day in the life Your focus centers on the software test infrastructure and hardware integration that validates our advanced robotic platforms. You'll develop and maintain automated test systems for engineering validation and manufacturing test while getting hands-on with hardware bring-up and debugging, working alongside hardware engineers, firmware developers, and fellow technicians. Your responsibilities include writing test automation code, building HIL test environments, bringing up new hardware revisions, debugging SW/HW integration issues at the bench, analyzing test data, and designing test fixtures. Throughout the day, you balance developing new test capabilities with hands-on hardware integration, supporting urgent prototype bring-up requests, maintaining test infrastructure, and preparing test readiness for upcoming milestones. You're switching between writing code at your workstation, probing signals and validating hardware at the bench, collaborating in design reviews with engineers, and ensuring test lab equipment is calibrated and maintained. About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Alexa AI is looking for a Senior Applied Scientist to build Alexa+, Amazon's LLM-powered conversational assistant. You will work on key initiatives spanning large language model fine-tuning, alignment, agentic reasoning, and evaluation — directly shaping the experience for hundreds of millions of customers worldwide. As a Senior Applied Scientist, you are a strong technical contributor who independently drives complex projects from ideation to production. You design and run rigorous experiments, develop novel approaches to challenging problems, and deliver high-quality models and systems at scale. Your work is characterized by scientific rigor, engineering excellence, and a focus on measurable customer impact. You collaborate effectively across teams, contribute to scientific discussions and reviews, and help elevate the technical bar within the organization. You proactively identify opportunities, propose solutions, and influence technical direction within your project area. Basic Qualifications - PhD/MS in Computer Science, Electrical Engineering, Machine Learning, Natural Language Processing, or a related technical field, OR Master's degree with 5+ years of relevant industry experience - 3+ years of hands-on experience in applied machine learning, predictive modeling, or NLP - Strong programming skills in Python or a related language - Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) - Track record of delivering ML/NLP solutions from research to production - Experience working with large language models (training, fine-tuning, or evaluation) Preferred Qualifications - 5+ years of relevant industry or academic research experience - Experience with LLM alignment techniques (RLHF, DPO, constitutional AI) - Experience with agentic AI systems, including planning, tool use, and orchestration - Experience with distributed training and large-scale model optimization - Peer-reviewed publications at top-tier venues (e.g., NeurIPS, ICML, ACL, EMNLP, ICLR) - Strong communication skills with the ability to present complex technical concepts to diverse audiences - Experience mentoring junior scientists or engineers Key job responsibilities - Design, implement, and evaluate novel approaches to LLM fine-tuning, alignment (RLHF, DPO), and distillation for production deployment - Develop and improve agentic systems — including multi-step reasoning, tool use, planning, and orchestration — that work reliably at scale - Build evaluation frameworks and methodologies that go beyond standard benchmarks to capture real-world conversational quality - Translate research advances into customer-facing products, working closely with engineering, product, and cross-functional science teams - Analyze large-scale experimental results, identify patterns, and iterate rapidly on model improvements - Publish results at top-tier venues and contribute to Amazon's presence in the broader research community - Mentor junior scientists and contribute to hiring efforts About the team Alexa AI is building the science and technology behind Alexa+, Amazon's next-generation conversational assistant. Our team works at the intersection of large language models, reinforcement learning from human feedback and verifiable rewards, agentic architectures, and multilingual/multimodal understanding. We operate at massive scale — our models serve customers across dozens of languages and device types. If you want to push the frontier of conversational AI and see your work used by people every day, come join us.

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, the Amazon Global Supply Chain Optimization Technology (SCOT) organization is for you. Watch this video to learn more about our organization, SCOT: http://bit.ly/amazon-scot We are the Optimal Sourcing Systems team (OSS) within SCOT and are looking for a Senior Applied Scientist to join us! OSS designs and builds systems that measure and manage Amazon’s supplier capabilities, identify and react to supply disruptions, and prioritizes inbound freight for our global network. OSS software is used by every country Amazon services, and is a critical link to ensuring Amazon offers the products our customers want, at the lowest possible cost. This team under OSS orchestrates and tracks inventory movement into Amazon's network, maintains performance feedback loops, and ensures vendor compliance. The Senior Applied Scientist, in partnership with the Product Management and Tech teams, will lead efforts in following areas: 1) Provide technical leadership and mentorship to the Science team, setting the standard for methodological rigor, peer review, and innovation across all workstreams 2) Build solutions to enable collaborative inventory planning with vendors through agent to agent collaboration or humans-in-the loop collaborative methods 3) Pioneer Gen AI solutions for dispute evaluation and vendor coaching, defining the technical approach, evaluating model performance against business outcomes, and establishing responsible AI guardrails for production deployment 4) Drive the full development cycle from whiteboarding new algorithmic approaches to production-scale deployments 5) Collaborate with SDEs to build high-performance, distributed training and inference pipelines; translate complex scientific concepts into scalable, production-grade code The ideal candidate is a seasoned scientist who thrives in ambiguous, high-impact problem spaces and brings the technical depth to independently structure and solve complex challenges across the supply chain. The successful candidate will be a person who has deep understanding about machine learning/reinforcement learning/GenAI models, enjoys and excels at diving into data to analyze root causes, and implementing long term solutions. They can translate complex business logic into scalable models and communicate insights effectively to both technical and non-technical stakeholders. Keys to success in this role include exceptional Science depth and breadth, analytics, statistics, judgment, and communication skills. Experience with supply chain optimization, operations research, or vendor management systems is a plus. Key job responsibilities Set the technical vision and drive best practices for the team's science solutions, including model evaluation frameworks, experimentation standards, code quality, and documentation Mentor junior scientists and raise the bar across the organization Lead cross-functional collaboration with product managers, science, and engineering teams to define problem frameworks, identify high-impact opportunities, and architect end-to-end model solutions for Sourcing Execution & Performance systems Design and execute rigorous studies and predictive modeling pipelines on large-scale datasets and experiments, establishing methodological standards for statistical validity, reproducibility, and business interpretability Partner with engineering to productionize science workflows driving the automation of analysis processes, building scalable measurement solutions, and ensuring models are robust, monitored, and maintainable in production environments

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions, and live events like Thursday Night Football. Within this expanding ecosystem, Linear TV with its 24/7 scheduled broadcast-style programming has emerged as one of our fastest-growing segments, with viewership hours increasing significantly year over year. This growth demonstrates that even in the streaming era, customers deeply value the lean-back, curated experience that Linear TV provides. Key job responsibilities As an Applied Scientist on LPEX, you will be a technical owner and science leader across the following areas: * Design, develop, and deploy machine learning models for content recommendation, viewer engagement optimization, and real-time personalization at the scale of hundreds of millions of Prime Video customers. * Own the complete ML lifecycle: problem formulation, data analysis, feature engineering, model development, offline and online evaluation, and reliable production deployment. * Build and continuously optimize recommendation systems with strict real-time latency requirements, ensuring that personalization decisions are delivered at speed and scale. * Design and execute rigorous A/B and multivariate experiments to measure recommendation quality, understand causal drivers of engagement, and iterate rapidly toward customer impact. * Partner with software engineering teams to productionize ML models, defining requirements for serving infrastructure, data pipelines, and model monitoring and observability. * Collaborate with product managers and cross-functional stakeholders to translate ambiguous business problems into well-scoped, tractable science solutions. * Mentor scientists and engineers on the team, setting a high bar for scientific rigor, experimental discipline, and ML engineering best practices. A day in the life We are looking for an Applied Scientist who will own the end-to-end machine learning lifecycle from problem formulation and research through experimentation and production deployment, building systems that help millions of customers discover the right content at the right time. It's Day 1 for personalizing the linear TV experience on Prime Video, and you will be at the forefront of this innovation. About the team The Linear Personalization Experience (LPEX) team is building next-generation, AI-powered personalization and recommendation systems to enhance this natural engagement and deliver a best-in-class Linear TV experience for Prime Video customers worldwide. The LPEX team's vision is to surface the breadth and depth of Prime Video's linear selection at exactly the right moment for each customer curating the most relevant programming, tailored to individual tastes, purchase behaviors, schedules, and viewing habits, while simultaneously elevating awareness of our extensive live and linear catalog. Our mission is to anticipate and exceed viewers' expectations, fostering deeper connections with the content they love. We adapt to viewers' preferences and propensities for both live and on-demand viewing, enriching the overall entertainment journey. The team operates at the intersection of machine learning research, large-scale distributed systems, and consumer product strategy, partnering closely with product management, engineering, and business development.

About Sponsored Products and Brands The Sponsored Products and Brands (SPB) 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. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As an Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.

Amazon Customer Service (CS) Data Intelligence builds the data and Artificial Intelligence (AI) foundations for CS to ensure Amazon delivers the best customer service possible. CS Economics sits within CS DI and contributes to the CS knowledge base and decision frameworks. CS Economics seeks economists to apply economic methods to solve business problems. The ideal candidate will work with engineers and applied scientists to design models that leverage large scale and unstructured data, design scalable agents for non-tech CS partners to understand the impact of their actions, and propose mechanism designs to robustly match customers to our services. CS Economics is looking for optimistic critical-thinkers who combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates enjoy working in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities - Design and conduct rigorous evaluations of CS actions - Develop experiments to evaluate product launches - Communicate complex findings to business stakeholders in clear, actionable terms - Work with engineering teams to develop scalable tools that automate and streamline evaluation processes A day in the life Work with teammates to apply economic methods to business problems, e.g., identify the appropriate research question and identification strategy, write code to estimate heterogeneous treatment effects or conduct experiment analysis, write and present a document with findings to business leaders. We collaborate with partner teams within and outside of CS throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team Amazon Customer Service (CS) Economics provides estimates and measures of the causal impact of CS actions on costs and benefits. We build agents and guide leadership to establish processes to scale valid experimentation, causal inference, and mechanism design.

Amazon is looking for a passionate and inventive scientist to advance the science in foundational models and Agentic AI. Specifically, as part of our science team in Amazon AWS Agentic AI, you will lead the research and development of techniques for efficient and effective Agent optimization/learning, through various techniques ranging from model fine-tuning (e.g. RL) to context optimization, as a foundational layer for AWS customers to build reliable and performant Agents. You will have the opportunity to take a product from zero to one by influencing directly the product and science roadmap, and impact millions of our customers. You will also gain hands on experience with Amazon’s large-scale computing resources to accelerate advances in foundation models. Key job responsibilities * Develop short-term and long-term science roadmap for research in the broad area of Agent optimization/learning, which could range from RL reward shaping, training efficiency optimization, to automatic prompt optimization and techniques to learn from agent memory. * Develop concrete science plan, implement and validate research idea before moving it to production * Collaborate across product and engineering teams to transfer science innovations into AWS customer facing product. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.