Careers

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 robotic systems that combine frontier AI, sophisticated control, and advanced mechanical design to create adaptable automation capable of working safely alongside humans in dynamic environments. This is an 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 manipulation, locomotion, and human-robot interaction. We are seeking a Senior Applied Scientist to develop tactile- and force-driven manipulation policies for highly dexterous, multi-fingered robotic hands and grippers. In this role you will research and build policy learning algorithms that enable robotic hands to perform grasping and in-hand manipulation in unstructured, real-world environments, using touch and force feedback as first-class signals rather than afterthoughts. Much of this work is grounded in reinforcement learning. To be successful you need to be highly motivated, dive deep, and deliver to the highest standards. You will demonstrate strong working knowledge of modern policy learning methods, with expertise in taking algorithms from simulation onto real hardware. That means being comfortable working against real physical constraints and treating sim-to-real as a core research problem rather than optimizing for benchmark numbers alone. You will work across tactile signal processing, contact-rich manipulation, sim-to-real transfer, and multi-modal sensor fusion to build robust solutions for autonomous grasping, dexterous re-orientation, and fine motor control. You will bring the desire to learn from new challenges, and the problem-solving and communication skills to work within a highly interactive and experienced team. Key job responsibilities - Design and train manipulation policies that leverage tactile and force feedback for dexterous grasping, in-hand manipulation, and contact-rich tasks, using reinforcement learning, imitation learning, or hybrid approaches. - Drive sim-to-real transfer, closing the gap between simulated and physical performance on real robotic hands. - Shape objectives and control strategies with the hardware in mind, accounting for how motors and actuators actually behave: transmission ratios, torque versus power, backdrivability, and thermal limits. - Build simulation-based and on-robot evaluation frameworks, with benchmarks and metrics that make tactile perception and policy performance systematically comparable across iterations. - Own scientific and technical projects within the tactile sensing and manipulation workstreams, driving from research concept through deployment on physical robotic systems — and, at the senior level, set direction across a workstream. - Collaborate with hardware, mechanical design, firmware, and controls teams — and with partner applied science organizations — to translate research advances into deployable robotic capabilities, and to inform sensor and actuator design decisions with policy-level requirements. - Publish at top-tier venues and build collaborations with the external research community. - Contribute to a strong scientific bar on the team through code and design review, and mentor engineers and interns working on real-world manipulation and sensing problems. A day in the life Your morning might start with a standup alongside hardware and controls engineers reviewing overnight sim-to-real training runs on a multi-finger gripper, debugging why a grasp policy that worked in simulation is slipping on a real sensor array. Mid-morning you join the weekly tactile sensing workstream to align on sensor integration milestones and share early results from a contact-state estimator you have been prototyping. After lunch you spend a focused block iterating on a reinforcement learning reward formulation, testing variations in simulation before queuing runs on the cluster. Later you pair with a mechanical engineer to review sensor placement trade-offs on an upcoming gripper revision, then wrap the day by drafting a short experiment write-up and sketching next steps for a conference submission. No two days look exactly the same: one week you may be collecting teleoperated demonstrations on the physical robot to seed an imitation learning pipeline; the next you could be deep in a codebase refactor to support a new tactile modality. Throughout, you balance hands-on research — writing algorithms, running experiments, analyzing data — with cross-team collaboration and mentoring. The common thread is moving from research insight to working capability on real hardware, with a tight feedback loop between simulation and the physical world. About the team Our is developing next-generation manipulation capabilities for highly dexterous multi-fingered grippers and hands. Our work spans tactile sensor development, manipulation policy learning, and the tight integration of sensing hardware with intelligent control software. We are building systems that can feel, adapt, and manipulate with human-level dexterity. You will join a team working at the frontier of tactile sensing and contact-rich manipulation, helping define how robots perceive and interact with objects through touch. Because we build the hands as well as the policies that drive them, you will have a direct line to the sensor, actuator, and mechanism designs your algorithms depend on — and real influence over how they evolve. This is an opportunity to solve hard problems across perception, learning, and physical interaction alongside a team of scientists and engineers building toward real-world deployment.

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 The General Shopping Intelligence (GSI) team is a highly motivated, collaborative, and fun-loving group with a strong entrepreneurial spirit and bias for action. We provide advanced real-time machine learning services that connect shoppers with the right ads across all platforms and surfaces worldwide. Through deep understanding of both shoppers and products, we help shoppers discover new products they love, enable advertisers to reach their customers most efficiently, and help Amazon continuously innovate on behalf of all customers. We are seeking a motivated Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, generative AI and high-scale machine learning systems. 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. Key job responsibilities As an Applied Scientist, you will: * Leverage Generative AI and Large Language Models (LLMs) to mine complex behavioral data, deriving deep, actionable shopper insights that identify customer experience gaps and unlock new business opportunities. * Design and develop scalable machine learning and GenAI models focused on shopper intent and preference modeling, ensuring a rapid path from prototype to production. * Partner closely with engineering teams to architect and deploy end-to-end GenAI solutions into production, integrating advanced insights directly into real-time, customer-facing systems. * Drive the scalability, efficiency, and automation of large-scale model training and real-time inference systems, pioneering the LLM infrastructure required to support next-generation GenAI workloads at Amazon Ads scale. * Design and run rigorous A/B experiments to quantify the business and customer impact of GenAI-driven shopper insights, performing advanced statistical analysis to guide iterative production rollouts. * Conduct applied research in novel generative AI techniques (e.g., fine-tuning, RAG, agentic workflows) to optimize the shopper experience and drive performance across all aspects of the Sponsored Products and Brands business.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.

As an Applied Scientist in Amazon Fullfilment Technology, you will lead the development of agentic systems to assist with operational decision making and orchestration. You will work building full agentic systems leveraging multi-agent orchestration, tool use, memory, and action execution. You will train LLMs using a combination of rejection sampling approaches, SFT, continual post-training, and Reinforcement Learning (RL). These systems are deployed to Amazon buildings, and you will also work on rigorous offline and online evaluations. Your work will leverage the latest LLMs to develop capabilities for agentic reasoning, coding and analytics. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication. Key job responsibilities - Generating training and preference data for specific use cases (reasoning trajectories, tool traces) - Reward modeling and policy optimization for LLMs: DPO, IPO, RLHF/RLAIF with PPO/GRPO, rejection sampling. - Supervised fine-tuning on step-by-step trajectories and tool-use traces - Verbal Reinforcement Learning and Continual Learning - RL for LLMs, Offline RL and off-policy evaluation - Agentic memory/state management; episodic and semantic memory; vector search; grounding with RAG. - Evaluation: developing decision quality metrics, scaling LLM-based evaluations. About the team Amazon Fulfillment Technologies (AFT) powers Amazon's global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. Learn more about AFT: https://tinyurl.com/AFTOverview

Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world! Passionate about building, owning and operating massively scalable systems? Want to make a billion-dollar impact? If so, we have an exciting opportunity for you. The AWS Managed Operations (MO) organization was founded in April 2023, with the objective to reduce operational load and toil through long-term engineering projects. MO is building the best-in-class engineering and operations team that will own the day-to-day operations for AWS Regions, improving the availability, reliability, latency, performance and efficiency to operate AWS regions. The AWS Managed Operations Intelligence (MOI) Team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insight strategy for AWS. You will be expected to serve as a Full Stack Data Scientist. You will be responsible for driving data-driven transformation across the organization. In this role, you will be responsible for the end-to-end data science lifecycle, from data exploration, ETL, model development and data visualization. You will leverage a broad set of tools and technologies, including general analytical frameworks (Spark, Airflow, etc.), AI frameworks (Hugging Face, etc.) and various machine learning frameworks, to tackle complex business problems. Your analytics research will provide direction on the technology strategy of the Managed Operations organization. Your Decision Science artifacts will provide insights that inform AWS' Operations and Site Reliability Engineering teams. You will work on ambiguous and complex business and research science problems at scale, and you are comfortable working with cross-functional teams and systems. By working together on behalf of our customers, we are building the future one innovative product, service, and idea at a time. Are you ready to embrace the challenge? Come build the future with us. Key job responsibilities - Work with large and complex data sets to solve a wide array of challenging problems using different analytical approaches - Develop ML/AI models. Partner with software teams to productionalize these models. - Data Pipeline and Infrastructure: design and implementation of data pipelines - Metric Development and Monitoring: Define and develop advanced, customized metrics and key performance indicators (KPIs) that capture the nuances of the organization's strategic objectives and operational complexities. Continuously monitor and evaluate the performance of metrics A day in the life Why AWS? Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Diverse Experiences AWS 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. About 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. AWS Infrastructure Services (AIS) AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we're the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we're looking for talented people who want to help. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events create stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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. About the team The Managed Operations Intelligence (MOI) Team helps AWS operate its services across the world. We help monitor AWS operations by providing insights and recommendations on AWS operations.

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining advertising through generative AI technologies, changing how customers discover products and engage with brands across Amazon.com and beyond. We combine human creativity with artificial intelligence to improve every stage of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights. We are dedicated to developing responsible AI technologies that balance advertiser needs and improve the shopping experience. In this role, you will develop rigorous methods to understand how traffic sources, customer journeys, and onsite experiences influence purchasing and revenue. You will use experimentation, causal inference, statistical analysis, and machine learning to distinguish correlation from true business impact and identify the drivers of performance changes. You will work with product, engineering, and business partners to translate complex findings into clear recommendations, design measurable interventions, and build tools that support faster diagnosis and better investment decisions. If you are energized by solving complex challenges at the intersection of AI and measurement science, we would love to talk to you. Key job responsibilities - Develop methods to understand how traffic sources, customer journeys, and onsite experiences drive purchasing and revenue, using experimentation, causal inference, and machine learning to separate correlation from true business impact. - Drive or heavily influence the design of scientifically complex software solutions or systems, taking ownership of components and providing system-wide design guidance for both new and evolving systems. - Define a long-term science vision and roadmap, combining science leadership, technical depth, and business understanding. - Translate complex findings into clear recommendations for product, engineering, and business partners, and build tools that support faster diagnosis and better investment decisions. - Mentor junior scientists, raise the scientific bar across the team, and identify opportunities where generative AI can accelerate learning and efficiency. A day in the life You might start your morning reviewing anomaly detection outputs to understand a recent shift in traffic patterns, then move into a working session with engineers to refine how an attribution model is served in production. After lunch, you could lead a design review for a new quasi-experimental framework with product and finance partners. Later, you might pair with a junior scientist on their causal inference approach, helping them sharpen their methodology before presenting results to leadership. About the team The Sponsored Products and Brands team builds solutions that extend advertising campaigns beyond the Amazon store, reaching shoppers across third-party websites and apps where they search and shop. We pair large-scale, low-latency systems with advanced machine learning to deliver high-quality sponsored experiences to advertisers and shoppers alike. Within this team, the measurement program is a growing area of investment focused on understanding how shoppers arrive at Amazon and how that traffic translates into advertiser and business results. This is a high-visibility role where your work will directly inform investment decisions and product strategy. We are a distributed team of scientists, engineers, and product managers who partner closely with other cross-functional teams across the organization. If you want to shape how Amazon measures and improves the customer journey, this is the team to join.

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models large language models and agentic reasoning systems to life sciences problems then put them into the hands of pharma biotech and diagnostics customers as applications and managed services they can fine-tune tailor and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological regulatory and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. In clinical trials we're building AI that automates and optimizes regulatory and clinical development workflows. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor encourage publication and support conference participation. If you want to do research that ships this is the team. Key job responsibilities • Set the scientific vision and research agenda for LLM reasoning, agentic AI, and biological model customization across the portfolio • Innovate on LLM reasoning, planning, and agentic approaches for complex scientific and regulatory workflows • Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that enable customers to train better models on their own data with less effort • Advance methods to adapt and extend biological foundation models for customer-specific therapeutic applications • Solve open research problems in faithful reasoning, multi-step planning, and tool use in high-stakes scientific domains • Partner with Life Sciences domain experts and customers to understand their hardest scientific challenges and translate those into tractable research problems • Publish at top-tier venues and build the team's external scientific reputation • Mentor applied scientists across the team while maintaining significant personal research contribution • Collaborate with product and engineering to ensure research translates into shipped products that serve customers at scale • Influence multi-year research roadmaps through deep scientific expertise and customer understanding A day in the life • Push a new reasoning approach into production that measurably improves outputs for a pharma customer's workflow • Design and run experiments to validate a novel fine-tuning method then ship it as a capability customers can use immediately • Unblock a delivery milestone by diagnosing why a model is failing on a new class of inputs and implementing a fix • Meet with a customer's scientific team to scope what the next model release needs to do for them • Review a teammate's experimental results sharpen the approach and help get it over the finish line • Publish results from shipped work at a top venue closing the loop between research and impact • Prototype a new idea that could become the next major capability in the product About the team 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. 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. 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. 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. 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 on ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Scientist II, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

Are you passionate about solving complex problems and protecting one of the world’s largest cloud platforms? The AWS Payments and Fraud Prevention team is looking for an innovative Applied Scientist to help keep AWS a safe and trusted environment for millions of customers worldwide. In this role, you will design, build, and deploy machine learning models that detect, prevent, and mitigate fraudulent activity across the AWS ecosystem. You will work with massive, real-world datasets, develop new detection strategies, and apply advanced and practical technologies to tackle ever-evolving threats. You will also explore Generative AI (GenAI) techniques to uncover new fraud patterns and strengthen our fraud defenses. At AWS, we support hundreds of thousands of businesses, powering billions of transactions every day. Fraudsters are constantly innovating — and so are we. If you enjoy thinking like a fraudster, building resilient defenses, and making a real-world impact, we invite you to join us and help shape the future of secure cloud computing. Key job responsibilities * Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activities across the AWS platform. * Analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. * Explore and apply GenAI techniques, including large language models (LLMs), synthetic data generation, and adversarial simulations to enhance fraud detection capabilities. * Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. * Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics. * Continuously monitor model performance and improve robustness against adversarial behaviors and evolving fraud tactics. * Communicate findings and technical insights clearly and effectively to both technical and non-technical audiences. * Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. About the team AWS Payments and Fraud Prevent is responsible for detecting & mitigating AWS account risks. You’ll be part of a team of Scientists, Analysts, and Technical & non-Technical Program Managers. The team’s goal is to identify and neutralize fraudsters from unauthorized access to legitimate AWS customers accounts. We have a formal mentor search application that lets you find a mentor that works best for you. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.

Are you passionate about building machine learning systems that protect one the world's largest cloud platform? The AWS Payments & Fraud Prevention (P&FP) Science team is looking for a driven Applied Scientist to help safeguard AWS and its millions of customers from evolving fraud threats. In this role, you will design, build, and deploy end-to-end machine learning models that detect and prevent fraudulent activity. You will work with massive, real-world datasets across the AWS payments, signup and usage ecosystem, develop new detection strategies, and take models from concept to production. You will also apply Generative AI (GenAI) techniques to enhance fraud signal discovery and strengthen our detection capabilities. At AWS, we process billions of transactions every day for hundreds of thousands of businesses worldwide. Fraud patterns shift constantly and our defenses must stay ahead. If you enjoy owning problems end-to-end, shipping models that make real-time decisions at scale, and making a direct impact on customer trust and financial protection, we invite you to join us and help shape the future of fraud prevention at AWS. Key job responsibilities Design, build, and deploy end-to-end machine learning models and rules to detect, prevent, and mitigate fraudulent activities across the AWS payment and usage ecosystem. Source, extract, and analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. Apply hands-on expertise in statistical modeling, traditional machine learning, and analytics to identify and isolate issues across the fraud landscape. Explore and apply GenAI techniques, including large language models (LLMs) and synthetic data generation, to enhance fraud detection capabilities. Own the full model lifecycle — from data extraction and feature engineering through evaluation, productionalization, and deployment. Continuously monitor model and rule performance and improve robustness against adversarial behaviors and evolving fraud tactics. Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics with a focus on rapid time-to-production. Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. Communicate findings and technical insights clearly and effectively to both technical and non-technical stakeholders at all levels. Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. A day in the life You will have the opportunity to enhance our existing models and develop new ones that have a direct impact on the business from reducing financial losses to protecting customer accounts in real time. You will own your models end-to-end, from sourcing data and building features through evaluation and productionalization, and you will be expected to move quickly as fraud threats evolve. As part of this role, you will also support core fraud operations reviewing model outputs, tuning detection thresholds, and ensuring our mechanisms are performing as expected in production. This operational closeness to the data and to real fraud cases is what gives our scientists a unique edge: you will develop a deep, practical understanding of how fraud actually works, which directly sharpens the models and strategies you build. It is this combination of science and operational insight that makes our team's work so impactful. Your role will also allow you to leverage your customer-obsession skills by thoughtfully considering the user experience and ensuring it is not adversely affected by the mechanisms you design. You will explore new techniques, including Generative AI, to stay ahead of increasingly sophisticated adversaries. About the team Our team plays a crucial role in safeguarding secure and profitable business operations. Our mission is to position AWS as the most cost-effective and user-friendly cloud service provider by protecting legitimate customer experiences from the financial, operational, and reputational impacts of fraudulent activities. We develop services that prevent, detect, contain, and mitigate the actions of fraudulent and malicious users. As part of an analytics-driven team, you will leverage large-scale data to inform business decisions, respond to fraud, and automate decision-making at scale. Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. 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. About 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 (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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. 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.