Careers

Do you like working on projects that are highly visible and are tied closely to Amazon’s growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? The Amazon IESP Digital Payments & Emerging Team is hiring Applied Scientists to work in our Machine Learning team in São Paulo, Brazil. The Payments team designs and builds scalable payment systems that power Amazon's checkout and financial services across emerging countries (Brazil, Mexico, Australia, Singapore, and more). We tackle complex challenges in payment orchestration, fraud detection, and regional payment method integration to drive conversion and deliver seamless experiences at scale. Key job responsibilities About you You want to make changes that help millions of customers. You don’t want to make something 10% better as a part of an enormous team. Rather, you want to innovate with a small community of passionate peers. You have experience in analytics, machine learning, LLMs and Agentic AI, and a desire to learn more about these subjects. You want a trusted role in strategy and product design. You put the customer first in your thinking. You have great problem solving skills. You research the latest data technologies and use them to help you innovate and keep costs low. You have great judgment and communication skills, and a history of delivering results. Your Responsibilities - Define and own complex machine learning solutions in the consumer space, including targeting, measurement, creative optimization, and multivariate testing. - Design, implement, and evolve Agentic AI systems that can autonomously perceive their environment, reason about context, and take actions across business workflows—while ensuring human-in-the-loop oversight for high-stakes decisions. - Influence the broader team's approach to integrating machine learning into business workflows. - Advise leadership, both tech and non-tech. - Support technical trade-offs between short-term needs and long-term goals.

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers

External job description Job summary Amazon Publisher Services (APS) helps digital publishers around the world build and grow thriving businesses. We provide services and advanced technologies to web, mobile app and advanced TV publishers of all sizes, including many of comScore’s global top 100, to help them monetize their content with demand from multiple programmatic buyers. Our server-side header bidding solutions are fast and reliable across devices, handling billions of queries per day, delivering ads in milliseconds. The result is more profitable advertising for publishers and more relevant ads for customers. As a Data Scientist on this team, you will: • Solve real-world problems by getting and analyzing large amounts of data, diving deep to identify business insights and opportunities, design simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIE's, and Product Managers. • Write code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems. • Apply statistical and machine learning knowledge to specific business problems and data. • Build decision-making models and propose solution for the business problem you define. • Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. • Analyze historical data to identify trends and support optimal decision making. • Formalize assumptions about how our systems are expected to work, create statistical definition of the outlier, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. • Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing. Internal job description The candidate would work with Product, Engineering, BIEs and Scientist across Supply and Demand organization to help make APS the best performing supply path for Amazon ads advertiser customers. They would spearhead efforts to conduct experiments alongside demand and measurement teams to identify optimal perfomance path for advertisers while improving APS Share of Wallet. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing.

We are seeking an exceptional Applied Scientist, Seller Abuse Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to detect and prevent abuse of Amazon's catalog worldwide. You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and behavioral data to build detection systems that are ahead of evolving adversarial tactics. Key job responsibilities * Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale * Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels * Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience * Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas * Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness A day in the life Day-to-day you can expect to: - Explore datasets to understand predictors and patterns of abuse - Work with product, program, and engineering stakeholders to build solutions into production that will last - Identify new and emerging abuse vectors as abusers get more sophisticated - Use search, graph, computer vision, NLP, and anomaly detection methodologies to automatically detect abusive actions. About the team Seller Abuse Prevention detects abuse across 4 distinct spaces of abuse: catalog, review, financial risk, and discovery/competitor abuse. Seller Abuse Prevention is embedded in a team of scientists that tackle cross-spanning risk prevention problems. The team has expertise across graph networks, LLMs/agents, and fraud detection.

Are you excited about building the science behind marketing measurement for one of the world's largest B2B technology companies? AWS Marketing is looking for an Economist to help expand our measurement framework beyond demand generation — into brand, product-led growth (PLG), and developer programs. You will work to develop the models and analyses that quantify how these investments drive customer acquisition, engagement, and long-term value. In this role, you will contribute to the design and execution of measurement approaches for marketing investments that operate through mechanisms distinct from traditional performance marketing. You will help answer questions such as: What is the incremental impact of brand awareness on customer preferences and conversion costs? How do PLG and developer program investments translate into measurable business outcomes? How should we estimate returns at the program and customer level, not just the channel level? What is the right level of investment across these categories relative to demand generation? This role combines rigorous economics with applied problem-solving. You will build econometric models, run causal analyses, work with large-scale datasets, and translate findings into recommendations for marketing and finance stakeholders. You will operate in a space where established B2B methodology is limited — bringing structure, creativity, and intellectual rigor to novel measurement problems. Key job responsibilities Develop econometric and causal inference models to estimate the impact of brand, PLG, and developer program investments on acquisition, engagement, and revenue Build measurement approaches that produce program-level and customer-level estimates, extending the team's existing channel-level framework Connect upper-funnel metrics (awareness, consideration, salience) and product engagement signals to downstream business outcomes including revenue and customer lifetime value Apply methods suited to long-horizon effects with slow feedback loops — distinguishing these from shorter-cycle performance marketing signals Conduct empirical analyses using large-scale observational and experimental data, ensuring statistical rigor and reproducibility Partner with the senior economist leading this area to design research agendas, scope analyses, and iterate on methodology Communicate findings clearly to technical and non-technical audiences, including marketing leaders and finance partners Stay current on measurement literature across academia, consulting, and industry; bring relevant approaches into AWS's framework Collaborate with data scientists and engineers to operationalize models and integrate results into marketing science products About the team The AWS Marketing Science team is a group of scientists, economists, and engineers building science products that power marketing decisions across AWS. We measure outcomes, target customers, and forecast growth — and we consult with marketing stakeholders on how to optimize their investment. Our team has deep expertise in lower-funnel measurement (multi-touch attribution, incrementality testing, long-term ROI) and is now expanding into brand, PLG, and developer program measurement. You will be part of building that capability from the ground up.

The AWS Central Economics & Science team is looking for a PhD economist. The ideal candidate will be proficient in both reduced form and structural estimation and, most importantly, will be eager to learn new methods where applicable. The ideal candidate should be a problem-solver first, with an ability to bring theoretical frameworks to real-world business problems, working backwards from the business problem rather than from any particular solution method. In this role, you will become a subject-matter expert in cloud infrastructure, creating theoretical frameworks, data-driven insights, and statistical models to help AWS serve its total demand at a lower cost. You will work closely with finance, product, and engineering teams—as well as economists and other scientists—to understand complex systems and products, and will have the freedom to propose, explore, and deliver on a wide variety of projects that emerge from your research. Our team functions like a start-up within the AWS ecosystem—we have the freedom to identify greenfield problems that other economists have not explored yet, and build trust with the business through delivering valuable insights and policy changes. Most importantly, we solve problems at a massive scale and do it in a collaborative, curious, supportive, and fun environment. Key job responsibilities - Become a subject-matter expert in various areas of infrastructure, cost management, and transfer pricing. - Deliver insights that leads to policy changes through analysis, modeling, and theoretical frameworks. - Collaborate closely with non-economist business partners to communicate insights, implement solutions through production models, and develop a research agenda. About the team ACES works on high-impact projects for AWS service teams and leadership. This position will support the cost and transfer pricing optimization team to help AWS continue to scale its infrastructure efficiently in a fast-changing technological and competitive environment.

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 subscriptions such as Apple TV+, HBO Max, Peacock, 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 team member, 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! Key job responsibilities • Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform. • Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models. • Run experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative machine learning approaches. • Share knowledge and research outcomes via internal and external conferences and journal publications About the team Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable, and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies across Prime Video’s global content and marketing pipelines. This is a newly formed team within Prime Video Science!

Amazon Leo is Amazon’s low Earth orbit satellite broadband network. Its mission is to deliver fast, reliable internet to customers and communities around the world, and we’ve designed the system with the capacity, flexibility, and performance to serve a wide range of customers, from individual households to schools, hospitals, businesses, government agencies, and other organizations operating in locations without reliable connectivity. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. As a Senior Data Scientist (DS), you will drive the development and implementation of advanced analytics and machine learning solutions. You will work on critical initiatives including natural language processing for non-conformance analysis, statistical process controls (SPC) for test optimization, and equipment predictive maintenance models to enable manufacturing rate acceleration. Your work will directly influence Kuiper’s production manufacturing workflow. You are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers, and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. Key job responsibilities Lead the design and implementation of ML/LLM solutions to analyze manufacturing data and identify failure patterns and operational risks Design predictive models for statistical process control and equipment maintenance optimization Build production-ready ML pipelines leveraging AWS services (e.g., SageMaker, Bedrock, AWS Glue) Formalize assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them Develop and test model enhancements, running computational experiments, and fine-tuning model parameters for new models Collaborate with engineering teams to translate complex manufacturing challenges into data-driven solutions Drive consensus on metrics and analysis approaches to support business strategy Write documents and create compelling visualizations and presentations to communicate insights to stakeholders Mentor team members and drive data science best practices across the organization About the team Established in 2023 as ProdOps was preparing to transition from development into full-rate production, OCC was purpose-built to ensure that as manufacturing scaled, the most critical problems would be identified, solved, and prevented from recurring through a single integrated operating system. OCC focuses on the most critical problems for ProdOps at each phase of production. The team operates as a closed-loop flywheel where each rotation identifies value, solves problems, implements change, and generates new data that feeds the next cycle. Every rotation compounds returns.

As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment. • Formulate fulfillment operations problems (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 3+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems