Careers

In Amazon Advertising, we apply machine learning at massive scale to optimize the prediction, ranking, and bidding behind every ad — deciding, in milliseconds, which ads to show shoppers and how to value them. We're looking for an Applied Scientist to help make sure the ads shoppers see are the right ones for them. You'll work across the science of how we rank, value, and bid on ads for Amazon DSP (Amazon's Demand-Side Platform) — including how we judge whether an ad is a good fit for the page a shopper is on and for the shopper themselves. It's high-scale, low-latency, customer-facing science: your models run live in front of millions of shoppers under tight real-time constraints. The questions are genuinely open — how do you tell whether an ad is relevant to someone, how do you balance what's good for shoppers, advertisers, and Amazon, and how do you keep getting that right as shopping behavior and inventory shift underneath you? Your work will have real impact, and you'll have room to shape where we take it. A few things make this stand out: your models touch a huge share of the ads shoppers see every day, so even small improvements add up fast; you'll run modern ML live under strict latency limits, across regions and very different types of ad inventory; and the problem space is rich — from how we value and bid on ads, to keeping models stable as traffic shifts, to what makes an ad a good fit for a shopper. Key job responsibilities - Design and improve the models that decide how ads are ranked, valued, and priced — including how relevant an ad is to the page and the shopper. - Apply and extend state-of-the-art techniques across e.g. ranking, deep learning, and information retrieval. - Own problems end to end: frame them, prototype, experiment, and ship them to production. - Balance competing objectives — shopper experience, advertiser and publisher value, and Amazon's business — into models that hold up across placements and marketplaces. - Communicate your work clearly to both business and science audiences, tailoring how you share it to each. - Write and ship your own production code backed by strong engineering support — we're all builders here. - Move fast with the best tools available, including modern AI coding assistants and agents. A day in the life You might start by digging into last week's experiment results, then use an AI coding agent to get your next prototype built and ready to test in production. In the afternoon you could be sketching a new way to measure ad relevance, reading a recent paper that bears on it, and talking it through with a senior scientist on the team. You'll move between hands-on science, writing and shipping real production code, and making the calls on your own work. About the team We're a group of scientists and engineers based in Edinburgh and London, working to make Amazon's ads more performant and relevant. We sit within a larger team spread primarily across New York City and the UK, and we have a broad mandate to build and experiment. You'll work alongside senior applied scientists you can learn from, with the data and infrastructure to do the work well and room to grow — with opportunities to attend top conferences (e.g., NeurIPS, KDD, ICML) and take on more scope over time.

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle unparalleled object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to advance reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will focus on training these policies in simulation at scale and transferring them to physical robots. You will join a small team whose mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. Key job responsibilities - Design and train reinforcement learning policies for non-prehensile and contact-rich manipulation, focused on the long tail of diverse, demanding conditions. - Build and scale simulation environments, training curricula, and reward formulations that produce policies which transfer to real robots. - Develop sim-to-real methods (domain randomization, system identification, and related techniques) and validate them on physical hardware. - Write production-quality code and own scalable, efficient training and evaluation pipelines. - Evaluate policy behavior and failure modes, and iterate between simulation and real-world testing to improve robustness. - Partner with scientists and engineers across control, perception, and hardware to move ideas from prototype to demonstrated capability. - Represent Amazon Robotics in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that assist you and eligible family members, including domestic partners. 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: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 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!

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments, at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to bring learned manipulation policies to life on real robots. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will focus on making learned, contact-rich, non-prehensile behaviors work reliably on physical hardware, closing the loop between what a policy does in simulation and how it behaves against real contact, sensing, and dynamics. You will join a small team whose mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to test new policies and behaviors on real hardware faster than almost anywhere in the field. Key job responsibilities - Deploy and evaluate learned manipulation policies on physical robots, focused on the long tail of diverse, demanding conditions. - Diagnose the gap between simulated and real behavior, and drive the changes in policies, rewards, sensing, or control that close it. - Develop tactile, actuation, and contact-aware methods that make non-prehensile manipulation fast, assured, and robust. - Write production-quality code and own scalable, real-time implementations that run on robots. - Build the data collection, logging, and evaluation loops that turn real-world experience into policy improvements. - Partner with scientists and engineers across control, perception, and hardware to move ideas from prototype to demonstrated capability on hardware. - Represent Amazon Robotics in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that assist you and eligible family members, including domestic partners. 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: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 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!

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments, at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an experienced Senior Applied Scientist to help guide a small team advancing reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will set the technical direction for how we learn these behaviors, from simulation training through reliable execution on physical robots, and you will demonstrate new manipulation capabilities on real hardware at scale. This team's mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. You will raise the bar for scientific rigor and engineering quality, and mentor other scientists as the team grows. Key job responsibilities - Set the technical direction for learning non-prehensile and contact-rich manipulation policies, from testing the latest advances in the field through demonstrated capability on hardware. - Oversee the development of reinforcement learning approaches that address the long tail of diverse, demanding manipulation conditions. - Own the path from simulation training to reliable, real-time execution on physical robots, making evidence based calls on where learned approaches should replace engineered ones. - Demonstrate new manipulation capabilities on real robots at scale, and turn one-off results into repeatable methods. - Establish the standards, evaluation practices, and data-informed improvement loops that the team builds on. - Mentor scientists and engineers, and raise the bar for applied science rigor and engineering quality. - Partner across control, perception, and hardware to integrate learned behaviors into working systems. - Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. 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: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 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

We are seeking a Product Manager, Data Strategy & Physical AI to define and execute the long-term product vision for FAR's AI-powered robotics platform. The intersection of foundation models and physical intelligence is creating a once-in-a-generation opportunity to reimagine how intelligent systems perceive, reason, and act in the real world. We need a visionary product leader who can treat data as our primary competitive moat and translate research frontiers into scalable, production-grade capabilities. In this role, you will champion our core data strategy for foundation model creation, building a partner and tool ecosystem to systematically acquire, label, and iteratively improve physical AI datasets. You will architect a continuous data collection flywheel across deployed robot fleets, transforming real-world kinematics, video, and force-torque telemetry from edge operations back into high-fidelity training tokens. Recognizing the limitations of real-world environments, you will also lead the strategy to create high-fidelity synthesized datasets, utilizing advanced physics engines and simulation to generate diverse training tokens at massive scale. Key job responsibilities Data Acquisition & Labeling Ecosystem: Establish the partnerships, tools, and vendor pipelines necessary to acquire, curate, and continuously label multi-modal datasets for training large-scale models. Fleet Data Flywheel Infrastructure: Architect the framework for a continuous data flywheel that securely streams high-frequency kinematics, egocentric video, and force-torque telemetry from real-world robot fleets back into the training loop. Synthetic Data & Simulation Strategy: Define the strategy for generating high-fidelity, physics-aligned synthesized datasets using advanced simulation environments to scale training tokens for edge-case scenarios and long-horizon tasks. Data Compliance & Governance: Partner with operations, privacy, legal, and security teams to build enterprise-grade data management pipelines that programmatically enforce data minimization, anonymization, and CCPA/GDPR compliance. Data Quality & Token Curation: Implement automated telemetry filtering and dataset pruning strategies to identify high-value operational logs, eliminate redundant fleet data, and optimize training compute costs. Cross-Functional Physical AI Delivery: Act as the strategic bridge between machine learning research scientists, simulation developers, robotics engineers, and hardware teams to deliver data-ready platform features that improve physical reliability. 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.

Amazon Search is reinventing how customers find products through natural-language and semantic understanding. We are looking for an Applied Scientist II to push the science behind Natural Language Search that interprets complex, constraint-rich shopping queries, retrieves and ranks the most relevant products. You will build and ship large-scale relevance and ranking models that measurably reduce the rate at which customers see irrelevant results, working on problems that span query understanding, semantic matching, and contextual ranking at Amazon scale. Key job responsibilities - Design, train, and ship deep-learning ranking and semantic-matching models that improve search relevance and reduce how often customers see irrelevant results, across hard query types. - Build the training data and evaluation methods that make these models work: synthetic and historical labels, hard-negative mining, and targeted sampling at the cases where search fails. - Develop signals that match product attributes to what the customer actually asked for. - Run offline and online A/B experiments, analyze precision/recall tradeoffs, and iterate to launch. - Work with engineers and scientists across teams to take models from prototype to production at Amazon scale. A day in the life You work alongside scientists and engineers on some of the hardest open problems in search relevance, teaching models to understand what customers really mean when they ask for something specific and nuanced. A typical day blends model development and data curation with sharp experiment analysis: diagnosing where search breaks down for a query segment, designing the fix, and proving the gains through offline metrics and live A/B tests that reach real Amazon customers. The work spans the full range, from surgical fixes that resolve stubborn failure pattern to broad modeling changes that move relevance for millions of queries at once. You'll see your ideas go from whiteboard to production fast, present results regularly to wider team, and help shape the team's relevance roadmap worldwide. About the team We are the science team behind Amazon's semantic search relevance and ranking. We own the models that understand nuanced, multi-constraint shopping queries and show products customers actually want. We operate close to production, measure ourselves on real customer-impact metrics, and run a culture of fast, rigorous experimentation. Every model decision is grounded in data.

We are Amazon's central Responsible AI team. Our mission is to advance the science and practice of Responsible AI (RAI) to enable Amazon's tens of thousands of builders to build and deploy AI solutions to the high standards that our customers and society expect. As a scientist on this team, you will: - understand in depth the technical and scientific issues related to RAI, including controllability, security, privacy, safety, veracity, robustness, fairness, explainability, transparency, and governance - help define the strategies, priorities and metrics for RAI - solve open problems in RAI to unblock traditional, generative and/or agentic AI use cases and solutions, publishing as appropriate - help develop our team - liase with internal and external stakeholders, including the academic community, on issues related to RAI About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

The Central Learning Solutions (CLS) Science team in Amazon Operations builds state-of-the-art solutions for enhancing leadership and associate development across the organization. We develop technology and mechanisms for building personalized learning experiences based on the profiles of different learners and assess the post-training performance curves for different learner profiles. As a Data Scientist on the team, you will be driving the data science roadmap for the CLS Science team. You will leverage your knowledge in statistics and econometrics, estimate the causal impact of training interventions, recommend the right interventions for a given learner profile, and measure the post-launch success of these interventions through A/B weblabs. These insights will help in dynamically changing the training content of Learning & Development courses and unlock opportunities to improve both training effectiveness and learner experience. You will collaborate effectively with internal stakeholders and cross-functional teams for solving business problems, create operational efficiencies, and deliver successfully against high organizational standards. Key job responsibilities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and implementation. - Use advanced causal inference methodologies to estimate the learning curves for different learner profiles and the effectiveness of training content. - Perform statistical analysis and statistical tests including hypothesis testing and A/B testing. - Implement new statistical, machine learning, or other mathematical methodologies to solve specific business problems. - Present deep dives and analysis to both technical and non-technical stakeholders, ensure clarity, and influence the strategy of business partners. 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 Central Learning Solutions (CLS) is the centralized technical team that builds Learning & Development products for North America Operations employees. CLS Science teams owns the science components that power the different products on Impact Estimation, Self Serve Analytics, Content Understanding, and Personal learning.

The Ads Trust Science team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.

Do you want to define the multi-year science vision that transforms how millions of customers experience AWS products? Do you want to influence the AWS investment in GenAI technology and see the impact of your leadership moving the needle on billions of dollars of AWS business? Do you want to lead cross functional team that impacts multiple organizations (product, sales, marketing, finance) in AWS? Do you want to push the boundaries of AI/ML technology (e.g. multi-agent analytics system, agentic knowledge representation and management, graph neural networks, reinforcement learning, causal inference, optimization, and LLM-based forecasting models) to build scalable ML products that help AWS grow and delight our customers? The AWS Analytics Engineering (AAE) is at the forefront of leveraging cutting-edge AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their data. We are a cross functional org from decision science, ML products, data platform, and agentic analytics system. Our vision is to use artificial intelligence and machine learning to enable AWS product teams, product, and go to market leaders to drive product growth and create personalized, optimized, and simplified product experiences to delight our customers. We shape AWS product features (e.g. Console, Spot and Autoscaling), influence GTM efficiencies with customer propensity models, democratize data and insights access through multi-agent system, and influence AWS leaders’ product strategy. We are looking for a customer-focused, solutions-oriented Senior Applied Science Manager to lead and define the science and engineering strategy across AWS product organization. In this role, you will set the technical direction for agentic analytics products, build ML features for AWS products to optimize their operations, influence product growth related decision science for senior leaders, generate ML-driven sales leads for AWS GTM teams, innovate multi-agent analytics system, develop big data engineering system at the AWS data scale. You will partner directly with GMs, VPs, and senior product leaders from major AWS product management, marketing, and sales organization to translate complex business challenges into innovative scientific solutions that directly influence AWS's top line and bottom line. As a Senior Applied Science Manager , you will be the technical thought leader who establishes the science roadmap, analytics software development, drives cross-organizational alignment, and raises the bar for scientific rigor across the team. You will work cross organization from AWS product management, engineering, sales, marketing, and finance. You will operate effectively in ambiguous environments, exercise strong business judgment on high-impact decisions, drive the innovation and publication roadmap, and continuously push the frontier of what's possible with ML-driven product intelligence at AWS scale. Key job responsibilities Define and drive the multi-year science, ML product, and software engineering vision and roadmap for ML-powered product analytics across AWS Products, Marketing, and Sales organization - Build, lead, and develop a high-performing team of technical managers, applied scientists, and software engineers, including hiring top talent, managing performance, and growing careers through mentorship and promotion readiness - Partner with senior AWS leaders (GM/VP level) to identify strategic, data-driven opportunities and translate business objectives into high-impact scientific initiatives - Architect and guide enterprise-scale ML and agentic platform, including agentic system, knowledge representation, big data platform, deep learning, graph neural networks, reinforcement learning, causal inference, and forecasting models that predict business outcomes and enhance customer experiences - Manage cross-functional science and software engineering team to build ML driven products that are scalable and leading industry best practices at the AWS scale and speed - Invent, operationalize, and scale novel analytical frameworks and metrics that enable data-driven product growth and executive decision-making - Communicate findings, conclusions, and strategic recommendations to technical and non-technical business leaders across AWS - Mentor scientists and engineers, establish best practices for experiment design and model evaluation, and review technical artifacts to ensure quality - Identify and champion new science opportunities that expand AAE’s impact across AWS, building the case for investment and driving adoption A day in the life As a senior applied science manager in AAE org, you will shape the science strategy that underpins product decisions across multiple AWS organizations. You'll spend your time partnering with VPs and GMs to identify the highest-leverage science opportunities, architecting novel ML solutions to complex product challenges, and mentoring scientists across the team. You'll drive alignment across cross-functional stakeholders, ensure scientific rigor in our most critical initiatives, and communicate insights that directly influence AWS product roadmaps. You'll balance long-term vision-setting with hands-on technical leadership, diving deep into model architectures and data pipelines when needed while maintaining the strategic altitude to guide the team's direction. You will manage cross functional science and engineering team to build cutting edge agentic system and ML products that transform our product and customer experience. About the team We are a team of scientists and software engineers supporting AWS product leaders to make high-impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We come from diverse backgrounds in statistics, computer science, engineering, and business analytics. We specialize in the full end-to-end ML development process, including data ingestion, ETL, model development, and model deployment in production. We provide AI/ML services across decision science, ML products, multi-agent analytics systems, and data engineering platform. High Impact Projects: We work on high-impact, high-visibility projects that directly influence AWS product roadmaps and senior leaders' decisions. Supportive Team Environment: We are proud of our supportive and inclusive team culture, we have each other's back during ups and downs. Work-Life Balance: We believe 80% of value comes from 20% of work, so we always prioritize our backlog ruthlessly based on business value. Learning Opportunity: Extensive opportunities to understand AWS business and leverage state-of-the-art AI/ML and cloud technology.