Careers

RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one. Imitation learning enables our robot to mimic "expert" behaviors, derived from human demonstrations or algorithmic strategies. By utilizing state-of-the-art generative AI and similar methods, our wheeled-legged robot can significantly enhance its autonomy and manipulation skills. In this role, you will enable robots to autonomously generate actions from demonstrations and real-time sensor data. These processes may also incorporate responses to natural language commands, further advancing the robot's skills. We are seeking an expert in imitation learning and generative AI techniques that directly produce robot behaviors, along with a deep knowledge of both supervised and self-supervised learning algorithms. If you are passionate about pushing the boundaries of AI and eager to deliver innovative solutions, we invite you to join us in shaping the future of intelligent robotics. Key job responsibilities Develop imitation learning algorithms, such as diffusion policies, to enable robots to autonomously execute actions based on demonstrations and real-time sensor data. Design, test, and refine your algorithms to meet the demands of complex real-world autonomy and manipulation tasks. Construct a dataset for the imitation learning algorithm using human demonstrations or automated expert algorithms. Streamline the workflow to efficiently expand the imitation learning dataset with new tasks. Collaborate with the reinforcement learning team to innovate methods that leverage both simulated and real-world data. Implement deployment-ready code for the real robot, optimized for the robot’s computational constraints. Build, lead and mentor an exceptional team of software engineers. Provide expert guidance to product managers and executives for strategic decision-making. Create and maintain documentation, guidelines, and best practices to streamline knowledge sharing.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Demand Tech Experience (DTx), as an org, owns the Amazon Ads Console — the advertiser-facing workspace for creating and managing campaigns, reviewing performance, and acting on recommendations used by millions of advertisers. Our team, DTx Science, is the centralized science team within DTx, dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. In this role, you will work closely with business leaders, stakeholders, and cross-functional teams to drive program success through ML- and agentic-driven solutions. You will shape the applied science roadmap, promote a culture of data-driven decision-making, and deliver significant business impact for millions of advertisers worldwide and the company using advanced data techniques and applied science methodologies. Key job responsibilities As an Sr. Applied Scientist on this team, you will - Lead the development of agent evaluation, agent impact measurement, agent experimentation, and agent advertiser experience - Lead high-ambiguity projects and drive alignment across teams on science and engineering solutions. - Drive adoption of state-of-the-art scientific technologies in generative AI, reinforcement learning, causal inference, classical machine learning, and natural language processing to improve the team's existing science solutions. - Translate complex scientific challenges into clear and impactful solutions for business stakeholders. - Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. - Foster collaborations between scientists to move faster, with broader impact. - Regularly engage with the broader scientific community with presentations, publications, and patents. About the team The DTx Science team is the centralized science team within Demand Tech Experience (DTx), dedicated to improving and protecting the advertiser experience across ad consoles through applied science methodologies. The team drives impact through four core functions: Ad Console Personalization, Recommendation Development, Ad Console Experimentation, and DVA Agent Evaluation and Impact Measurement. Improve advertiser experience: Ad Console Personalization and Recommendation Development aim to improve the advertiser ad console experience by providing customized and personalized console experiences for each user and increasing their ad performance with relevant, science-backed onboarding and oppertunty recommendations. Protect advertiser experience: Ad Console Experimentation and DVA Agent Evaluation and Impact Measurement protect the advertiser experience by supporting the organization in making data-driven decisions to launch UX and agentic features that benefit advertisers.

Are you passionate about turning complex data into actionable insights that shape how millions of sellers and businesses experience Amazon? EU 3P Amazon Business are looking for a Senior Data Scientist who thrives on solving ambiguous, high-impact problems using advanced analytical and machine learning techniques. In this role, you will lead the design and delivery of data science solutions that drive measurable business outcomes. You will partner with cross-functional teams to identify opportunities, develop models, and translate findings into strategies that inform critical decisions. If you are curious, collaborative, and energized by working at scale, this is an opportunity to make a meaningful difference. Key job responsibilities - Lead the end-to-end design, development, and delivery of data science solutions for complex business problems, applying statistical modeling, machine learning, and quantitative analysis to generate actionable recommendations. - Evaluate and recommend appropriate data science strategies and methodologies by leveraging broad expertise across multiple disciplines, ensuring solutions are rigorous, scalable, and aligned with business goals. - Collaborate with engineering, product, and business teams to define problem statements, advocate for relevant data collection and instrumentation, and communicate insights that influence team strategy. - Drive data science best practices across the team by establishing standards for code quality, model validation, and reproducibility, while mentoring other data scientists and reviewing their analyses and models. - Identify gaps in existing metrics and measurement frameworks, propose new approaches, and proactively restructure data sources to improve analytical capabilities and operational excellence. A day in the life You start your morning reviewing model performance dashboards and identifying areas for improvement. By mid-morning, you are deep in collaboration with engineers and product partners, aligning on how your latest analysis will shape an upcoming launch decision. After lunch, you might prototype a new machine learning approach, run experiments, or write up findings in a document for leadership review. You also carve out time to mentor a teammate on a modeling challenge and participate in a science review, offering feedback that raises the quality bar for the group. You will work with global partner data teams. About the team Our team is focused on using data science to solve meaningful problems that directly impact Amazon's 3P business in Europe. We value intellectual rigor, creative problem-solving, and a collaborative environment where every team member's perspective matters. We are building toward a future where our models and analyses drive smarter, faster decisions at scale. If you want to join an inclusive team that encourages experimentation and continuous learning, we would love to hear from you.

We are seeking a Principal Measurement and Attribution Analytics scientist to further the development and application of analytics methods to examine the complex data flows of measurement, attribution and shopper analytics and to translate deep-dives into actionable insights for our product teams. In this role you will develop new tools to analyze our advertising data to help improve the performance of our bidding algorithms, targeting and relevance systems, help advance our supply strategy, and evaluate the adoption and impact of feature releases. Key job responsibilities - Analyze data trends regarding supply, optimization, ad load, and advertising mix effects that affect advertiser performance and contribute to achieving advertiser goals. - Present papers to senior leaders on issues like feature development impact on identity recognition rates, and changes of ad selection systems to improve fill rate highlighting insights that will inform our business development and engineering roadmaps. - Identify, standardize, and operationalize KPIs to effectively measure the performance of all systems involved in ad serving, and use trend insights to inform business priorities. - Partner with engineering teams to define data logging requirements and getting these prioritized in engineering roadmaps. - Validate financial models through analysis - Develop and own ad revenue and supply intelligence analytics decks that provide ongoing deep-dives A day in the life The Measurement and Attribution science leader will work closely with business leaders and engineers on developing science and analytics framework for Ads performance measurement and attribution products and programs as well as common data architecture that will optimize our data logging at different grains, and will allow data interoperability from bid flow to optimization to campaign delivery and performance. There will be an emphasis on understanding the impact of shopper journeys to and from Amazon properties and their implication on product development. The candidate will then analyze the data and present papers and ongoing reports on actionable insights. About the team The Ads Science Product Team's Mission: Work alongside those who need product data to apply objective perspective and business logic to uncover insights, advise strategic decisions, and adjust to industry changes.

Do you want to translate cutting-edge science into products that expand financial access for millions? At International and Emerging Stores Payments (IESP), we build payment solutions for communities and geographies that Amazon's selection and prices do not yet reach because of payment barriers. These markets make up roughly 80% of the world's countries and 85% of its population. As an Applied Scientist II on our team, you will design, build, and ship machine learning and generative-AI solutions that remove those barriers and advance financial inclusion, working at the intersection of research and production alongside scientists, engineers, and economists. If you are a curious, self-motivated researcher who turns ambiguity into clarity, we would like to hear from you. What you will work on: • Build models that recommend the most suitable payment method for each customer—for example installments, cards, or local payment options—and identify the right customers to reach, balancing customer preference, cost, and risk. • Take models to production behind a real-time, low-latency personalization layer that serves live shopping traffic at scale. • Build multi-agent generative-AI systems that turn unstructured inputs—customer feedback, documents, and operational data—into reliable, decision-ready insight, with verification that guards against model hallucination. • Help customers discover and adopt installments and alternative payment methods. • Design and analyze large-scale online experiments and causal measurement to validate real customer and business impact. Key job responsibilities • Design, develop, and evaluate machine learning and deep learning models, and deploy production-ready solutions to complex customer and business problems. • Conduct rigorous scientific research: form hypotheses, run experiments, evaluate model performance, and iterate on approaches within your domain of expertise. • Write clean, maintainable, and efficient code that integrates smoothly into larger systems and meets a high bar for operational excellence. • Partner with engineering to take your models to production, and with scientists across related payments ML teams to solve shared, complex problems. • Contribute to team roadmaps, priorities, and technical direction; mentor peers and provide constructive feedback on research and code. • Publish at peer-reviewed venues and file patent applications where appropriate. A day in the life You will start your day reviewing experiment results and refining model architectures based on new insights. Throughout the day you will alternate between writing research code, analyzing data, and discussing technical approaches with teammates, working closely with engineering to build your ideas and take them to production. You will regularly present findings and propose new ideas, and you may write research papers and submit patent applications. About the team We are the science team for International and Emerging Stores Payments. We build solutions that directly shape how customers pay and that drive conversion across Amazon's fast-growing international markets—and there is no shortage of hard science and engineering problems, since these markets span most of the world's countries and the majority of its customers. We value scientific rigor, creative problem-solving, and a genuine commitment to learning and growth. If you are excited about building impactful solutions and advancing financial inclusion through applied science, we would love to have you join us.

Do you want to translate innovative science into products that expand financial access for millions? At International and Emerging Stores Payments (IESP), we build payment solutions for communities and geographies that Amazon's selection and prices do not yet reach because of payment barriers. These markets make up roughly 80% of the world's countries and 85% of its population. As an Applied Scientist II on our team, you will design, build, and ship machine learning and generative-AI solutions that remove those barriers and advance financial inclusion, working at the intersection of research and production alongside scientists, engineers, and economists. If you are a curious, self-motivated researcher who turns ambiguity into clarity, we would like to hear from you. What you will work on: • Build models that recommend the most suitable payment method for each customer—for example installments, cards, or local payment options—and identify the right customers to reach, balancing customer preference, cost, and risk. • Take models to production behind a real-time, low-latency personalization layer that serves live shopping traffic at scale. • Build multi-agent generative-AI systems that turn unstructured inputs—customer feedback, documents, and operational data—into reliable, decision-ready insight, with verification that guards against model hallucination. • Help customers discover and adopt installments and alternative payment methods. • Design and analyze large-scale online experiments and causal measurement to validate real customer and business impact. Key job responsibilities • Design, develop, and evaluate machine learning and deep learning models, and deploy production-ready solutions to complex customer and business problems. • Conduct rigorous scientific research: form hypotheses, run experiments, evaluate model performance, and iterate on approaches within your domain of expertise. • Write clean, maintainable, and efficient code that integrates smoothly into larger systems and meets a high bar for operational excellence. • Partner with engineering to take your models to production, and with scientists across related payments ML teams to solve shared, complex problems. • Contribute to team roadmaps, priorities, and technical direction; mentor peers and provide constructive feedback on research and code. • Publish at peer-reviewed venues and file patent applications where appropriate. A day in the life You will start your day reviewing experiment results and refining model architectures based on new insights. Throughout the day you will alternate between writing research code, analyzing data, and discussing technical approaches with teammates, working closely with engineering to build your ideas and take them to production. You will regularly present findings and propose new ideas, and you may write research papers and submit patent applications. About the team We are the science team for International and Emerging Stores Payments. We build solutions that directly shape how customers pay and that drive conversion across Amazon's fast-growing international markets—and there is no shortage of hard science and engineering problems, since these markets span most of the world's countries and the majority of its customers. We value scientific rigor, creative problem-solving, and a genuine commitment to learning and growth. If you are excited about building impactful solutions and advancing financial inclusion through applied science, we would love to have you join us.

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites providing low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communication Systems Research Scientist, this role owns the research and system design of the radio resource management (RRM) and radio access layers of Amazon Leo’s direct-to-device (D2D) system, delivering 3GPP-compliant service to unmodified commercial handsets. The Role: Be part of the team defining the communication system and architecture of Amazon’s direct-to-device wireless network and analyzing its system level performance: beam and cell capacity, spectral efficiency, coverage, latency and service availability. This is a unique opportunity to innovate with few legacy constraints, in a segment where the standard itself is still being written. This role leads the research and system design of radio resource management (RRM) for a 3GPP Non-Terrestrial Network (NTN), where D2D upends terrestrial assumptions: a power-limited handset with a near-isotropic antenna, very large cells, hopping beams, large time-varying delay and Doppler, and scarce shared spectrum. RRM in time, frequency and spatial domains is the focus, but the role reasons across the stack, from L1/L2 up through RRC, NAS and 5GC interworking. Agentic AI is expected to be a standard part of the work for development, optimization, tests and debugging, with the scientist accountable for the algorithms, models and conclusions. 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. Key job responsibilities • Research, design and specify RRM algorithms for Amazon Leo’s 3GPP-based D2D system: MAC scheduling, link adaptation, power control, HARQ strategy, DRX, admission and congestion control, and load balancing, mapping 5QI and QoS flow requirements to scheduler behavior across voice, messaging, emergency and data services. • Treat beam management as part of joint resource optimization, not a standalone process, optimizing it with band assignment, packet scheduling and user pairing in multi-user MIMO (MU-MIMO). • Define the RRM framework for NTN conditions: earth-fixed and earth-moving cells, large time-varying propagation delay, ephemeris-assisted timing and Doppler pre-compensation, extended timing advance, selective HARQ feedback disabling, feeder link and satellite handovers, and interference and spectrum sharing across beams, satellites and terrestrial networks using the same MNO spectrum. • Design mobility and service continuity for a network where the base stations (i.e., satellites) move rather than the user: idle and connected mode mobility, location and time based conditional handover, cell reselection, paging, tracking area design, and NTN-to-terrestrial continuity. • Specify supporting L1/L2 elements with the PHY team: numerology under Doppler, PRACH and initial access, coverage enhancement through repetition, synchronization at low SNR, receiver abstraction, and FEC and BLER modeling for link adaptation. • Keep the radio design coherent with the networking layers: RRC and NAS, RLC and PDCP over long-RTT links, CU/DU split, NTN gateway and 5GC/EPC integration, and transport behavior. • Develop link-level and system-level simulators capturing constellation dynamics, beam patterns, handset characteristics, traffic models and RRM behavior, and use agentic AI across that loop: build and refactor simulation code, scale parameter sweeps, optimize scheduler and link adaptation parameters, explore configuration spaces too large to sweep by hand, maintain regression tests, and triage failures across logs, traces and over-the-air captures. • Translate research into system requirements and implementation-level specifications, and work with modem, payload, ground, RF, ASIC and Testbed teams through integration, field trials and link bring-up, root-causing gaps between simulation, implementation and over-the-air behavior in a fast-paced environment. • Represent Amazon Leo in 3GPP and other standards development organizations, develop and defend contributions on NTN and D2D work items, and contribute patents and publications.

Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. Come work at Amazon! The Role: Be part of the team defining the overall communication system and architecture of Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in integration, verification and testing of the systems especially focused on MAC and higher layer testing. This role will also be responsible developing and testing advanced L1/L2/L3 concept to improve the performance and reliability of the LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. In this role you will: - Work within a project team and take the responsibility for the Leo’s communication system design, system integration and verification. - Work as a part of the team in building a suite of system and network simulation services in Matlab / C++ / Python - Develop requirements from system level to HW/SW level and define test cases associated with the requirements. - Identify additional HW and SW that are needed for the purposes of verification and guide the HW/SW development team in the development of these test solutions/tools// - Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements - Write scripts / code for functions / features required for specific simulation, testing and verification of given RF system EXPORT CONTROL REQUIREMENTS 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.

Amazon Economics is seeking Structural IO Economist (STRUC) Interns who are passionate about applying structural econometric methods to solve real-world business challenges. STRUC economists specialize in the econometric analysis of models that involve the estimation of fundamental preferences and strategic effects. In this full-time internship (40 hours per week, with hourly compensation), you'll work with large-scale datasets to model strategic decision-making and inform business optimization, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available STRUC internships in 2027. Key job responsibilities As a STRUC Economist Intern, you'll specialize in structural econometric analysis to estimate fundamental preferences and strategic effects in complex business environments. Your responsibilities include: - Analyze large-scale datasets using structural econometric techniques to solve complex business challenges - Applying discrete choice models and methods, including logistic regression family models (such as BLP, nested logit) and models with alternative distributional assumptions - Utilizing advanced structural methods including dynamic models of customer or firm decisions over time, applied game theory (entry and exit of firms), auction models, and labor market models - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including pricing analysis, competition modeling, strategic behavior estimation, contract design, and marketing strategy optimization - Helping business partners formalize and estimate business objectives to drive optimal decision-making and customer value - Build and refine comprehensive datasets for in-depth structural economic analysis - Present complex analytical findings to business leaders and stakeholders

AWS Startups supports hundreds of thousands of founders globally — from first credit to scaled production workload. Data and machine learning are how we identify high-potential startups early, personalize the guidance we deliver, and decide where to invest next. We recently launched AWS Startup Advisor, an AI-powered service that brings AWS expertise directly into the developer tools founders already use, and we need a scientist to build the ML capabilities that power its proactive recommendations. In this role, you will own science problems end-to-end — from the data foundation that unifies signals about founders and their products, through model design and evaluation, to production deployment serving hundreds of thousands of startups. If you want your models to reach real founders the same week you ship them, this is the role. Key job responsibilities - Own the science for problem areas end-to-end: frame the problem, define the data strategy, build and evaluate ML models for recommendation systems, startup segmentation, and fraud detection, and deploy them into production. - Apply generative AI and large language models to personalize the technical guidance founders receive, including retrieval, ranking, and evaluation of LLM-powered experiences. - Design and run experiments that keep model quality quantified and defensible, making clear trade-offs between accuracy, latency, and cost as you balance rapid iteration with production reliability. - Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate results clearly to both technical and non-technical leaders. - Raise the scientific bar through design and code reviews, mentor other scientists and engineers, and contribute to the broader scientific community through publications or peer reviews. A day in the life You might start the morning digging into a messy dataset to uncover a new segmentation signal, then shift to reviewing an A/B test that measures how a recommendation model is changing founder engagement. After lunch you could pair with an engineer to optimize inference latency for a fraud-detection model, then join a product review where you present trade-offs between two ranking approaches for AWS Startup Advisor. Expect to regularly move between hands-on modeling work and cross-team conversations that shape what gets built next. About the team The AWS Startups team builds products and platforms that support startup customers at every stage of their journey — from onboarding and credit programs to AI-powered guidance and scale solutions. We partner with business development, field marketing, and solutions architecture teams worldwide, and our portfolio serves hundreds of thousands of startups globally. We are building the next generation of AI-native products that make world-class cloud expertise accessible to every founder, and you will help shape the scientific direction that gets us there.