Careers

The role will be key to building the products and experiences that will be used by Proserve and Frontier AI organization to deliver business outcomes to our customers Are you excited about building products that help Proserve and Frontier AI teams understand customer needs, develop the right solutions, and deliver them at scale? The Frontier AI tooling team is looking for a Senior Applied Scientist to take a high-impact work across our sales and delivery team. Our work spans GenAI agents that answer sales questions in natural language, knowledge graphs, recommendation systems, synthetic controls, account prioritization models, helping proserve delivery consultant accelerate delivery leveraging AI tooling and more. A key part of our effort will be to built a world class context layer that will improve the quality , accuracy , reliability and security of our agents. As a Senior Applied Scientist, you will own ambiguous, high-value problems end to end, from framing the question with business leaders through to a deployed model that changes how the field operates. You will set technical direction for problems that don't yet have a clear approach, choose the right method rather than the familiar one, and raise the bar for the science across the team. You will partner directly with customers across AWS Sales to understand the challenges they face and deliver solutions they trust and adopt. You must be an expert in advanced quantitative and machine learning methods, and equally strong at synthesizing and communicating insights to audiences of varying technical sophistication. You will influence decisions well beyond your own projects, and you will help grow the scientists around you through mentorship and technical leadership. Key job responsibilities * Own the full lifecycle of complex science problems: problem framing, data exploration, method selection, modeling, evaluation, and production deployment. * Set technical direction on ambiguous problems and make the design decisions that others build on. * Partner with business stakeholders across Sales & Delivery consultants to turn open-ended business questions into well-scoped science, and translate results into recommendations leaders act on. * Raise the scientific bar across the team through design reviews, mentorship, and hands-on guidance. * Communicate methods, trade-offs, and results clearly to both technical and non-technical audiences. * Use Python, PySpark, and SQL for data analysis and model development. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empowers us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity, inclusion) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

Amazon Ads runs one of the largest real-time decision systems in the world. To meet advertiser goals, we process >4 million ad requests per second, and this team decides which of them are worth the compute. We mine a petabyte-scale stream of traffic to tell in-demand, valuable requests apart from wasteful ones, filtering out the noise and forwarding only what brings value to advertisers, all within milli-seconds. Our solutions save millions in compute costs and drive long-term growth for Amazon's Retail and Marketplace businesses. Why you'll love this role: The scientific challenge here is scale and signal: large-scale ML on enormous, high-velocity data, optimization and auction dynamics, and causal measurement of what actually moves the business. Generative AI and LLMs are tools you reach for when they help, not the point of the work. The impact also reaches beyond Amazon: we donated the Dynamic Traffic Engine (DTE) to the IAB Tech Lab as an open-source framework that lets demand-side platforms signal which bid requests they value, so supply-side platforms send fewer wasteful ones and QPS waste drops across the whole programmatic supply chain. We are a highly motivated, collaborative team with an entrepreneurial spirit and a broad mandate to experiment and innovate. You will work alongside talented engineers, product managers, and scientists in a culture that rewards bias for action, innovation, and experimentation. What makes this role unique is the combination of scientific rigor with real-world impact at a scale very few systems reach. Your impact and career growth: Amazon Ads is investing heavily in AI and ML, creating room for scientists to innovate and make their mark. As an Applied Scientist, you will own the science for well-defined supply-optimization problems and take your work end to end, from framing the approach through experimentation and into production with measured business impact. You will partner closely with engineers to productionize your models and with product managers to tie your science to customer and advertiser outcomes. You will share your findings, begin to mentor teammates, and grow your influence on the team's technical direction over time. This is your opportunity to do high-rigor applied science in one of the fastest-growing businesses at Amazon. Learn more about Amazon Ads: https://advertising.amazon.com/ Key job responsibilities As an Applied Scientist in Amazon Ads, you will: - Conduct hands-on data analysis and build large-scale ML models and pipelines for supply optimization. - Take your models end to end, from proof of concept through experimentation, optimization, and deployment into production, working closely with software engineers to productionize them and with product managers and fellow scientists along the way. - Design and run A/B experiments and causal analysis to validate hypotheses, draw insights from petabyte-scale data, and measure real business impact. - Research and apply advanced ML, including generative AI and large language models, to demand prediction and traffic filtering. - Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, developing scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience.

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 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 clinical trials we're building AI that automates and optimizes regulatory and clinical development workflows. In drug design our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. 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. The Role We are seeking an Applied Scientist to build the models and methods behind our life sciences AI products with a primary focus on clinical trial operations and agentic reasoning. You will design train and evaluate systems that reason over complex clinical and operational logic and ship them into products customers use directly. You will work closely with senior and principal scientists on well-scoped research problems own your results end to end and see your work reach production. This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason plan and act in complex scientific domains while applying domain knowledge to ensure models produce scientifically valid outputs. The problems span multiple fronts: • How do you build LLM-based agentic systems that correctly reason over clinical protocols regulatory standards and complex multi-step operational workflows? • How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings? • How do you develop model customization methods (fine-tuning retrieval augmentation domain adaptation) that let customers get strong results from foundation models on their own data? You will focus on clinical trial operations (agentic automation structured reasoning evaluation domain adaptation) with opportunities to contribute across drug discovery (protein engineering antibody design) as the portfolio grows. You will own end-to-end scientific solutions from research through production and your work will directly shape the tools that scientists use daily. Key job responsibilities • Design train fine-tune and evaluate LLM-based agentic systems that reason over clinical protocols regulatory standards and operational workflows • Build rigorous evaluation harnesses and benchmarks to measure agent reliability faithfulness and failure modes in high-stakes domains • Develop model customization methods (fine-tuning RLHF retrieval augmentation domain adaptation) that help customers get better outputs on their own data with less effort • Contribute to graph-based and causal modeling approaches for clinical trial operations • Partner with Life Sciences domain experts product and engineering to translate scientific challenges into shipped capabilities • Own experiments end to end: problem framing implementation evaluation iteration and hand-off to production • Publish at top-tier venues where the work supports it • Contribute to drug discovery efforts (protein engineering antibody design) as opportunities arise A day in the life • Design and run an experiment to validate a new agentic reasoning or fine-tuning method then ship it as a capability customers can use • Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone • Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic • Meet with domain experts to scope what the next model release needs to do • Review results with a senior scientist sharpen the approach and get it over the finish line • Prototype a new idea that could become the next 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.

We are seeking an Applied Scientist to join Compass. In this role, you will develop the core Control Barrier Function (CBF) algorithms that form the mathematical foundation of the Compass safety system. You will ensure they don't just work in theory but perform reliably on real robots under real-world conditions. You will push the boundaries of concepts central to CBFs: computing robust invariant sets, designing hybrid system formulations that handle contact transitions and mode switches, and developing backup-set approaches that leverage learned policies and multiple controllers. A key challenge of this role is bridging the gap between the mathematics of set invariance and the realities of hardware, including sensor noise, model uncertainty, computational budgets, and discrete state transitions. You will ensure that these algorithms are not only provably correct but also implementable within a safety-critical architecture that must be certified by a third-party. You will contribute directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet. Key job responsibilities • Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope for each robot platform • Compute and refine invariant sets for complex, high-dimensional robotic systems, developing scalable methods that go beyond what existing analytical approaches can handle • Design formulations for hybrid dynamical systems, handling discrete mode transitions (e.g., contact/no-contact, stance/flight phases) with provable safety across switching boundaries • Address the theory-to-practice gap by developing methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency • Create reduced-order and full-order dynamics models with both white-box and black-box approach • Implement real-time optimization solvers that execute within the tight timing budgets of safety-critical control loops • Develop formal arguments and documentation sufficient to support third-party safety certification of algorithms • Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins • Contribute to the theoretical foundations of CBFs through publications at top-tier controls and robotics venues • Collaborate with perception, planning, locomotion, and manipulation teams to accommodate the needs of upstream and downstream systems 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: 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! About the team Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy: one that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance. Your work will underpin robots operating alongside people at Amazon's unprecendented scale.

Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics. You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses. Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges. Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Guide fellow scientists in solving complex technical challenges, from sim2real transfer to training RL policies - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

At Amazon, we’re inventing on behalf of customers, and with Amazon Leo, we’re redefining what global connectivity looks like. Our mission is to deliver fast, affordable broadband to unserved and underserved communities around the world through a constellation of low Earth orbit (LEO) satellites. Every system we build helps connect people to education, healthcare, opportunity, and each other. As a Data Scientist, you will be responsible for developing advanced analytics and machine learning solutions for user terminals. You will develop predictive models to proactively identify possible user terminal failures in the field. You will work in a collaborative environment with a multi-disciplinary team, including constellation, RF, antenna, silicon, algorithm, and software engineers. Key job responsibilities As a Data Scientist, you will develop analytic tools for a team developing current and future user terminals. Your responsibilities include: • Develop statistical and analytical tool to enable the regression decision from on-orbit and lab measurement of user terminals • Publish documents and create compelling visualizations and presentations to communicate insights to stakeholders • Create and manage datasets for continued pre-training and supervised fine-tuning of LLMs • Develop scalable visualizations for analysis of user terminal performance • Work closely with constellation, RF, antenna, silicon, algorithm, and software engineers to root-cause the failures using data as the primary tool • Drive consensus on metrics and analysis approaches to support product development strategy 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. A day in the life As a Data Scientist in the LEO Customer Terminal Team, you will work daily with satellite constellation, algorithm, RF, antenna, silicon, hardware, and software teams in a collaborative environment. Your focus will be using data as an intelligence source to enable design decisions for the team. About the team The LEO Customer Terminal team is responsible for developing both outdoor and indoor devices that enable customers to access internet service via the LEO satellite network. We own the entire process from early prototypes through mass production, including requirements documentation, architecture definition, hardware development, algorithm development, and all integration and verification testing.

Amazon Leo is building a constellation of thousands of low Earth orbit satellites to deliver fast, reliable internet beyond the reach of existing networks. As a Guidance, Navigation & Control engineer, you will design the algorithms, simulations, and onboard autonomy that keep every satellite precisely pointed and stable so the payload can serve customers below, and keep the fleet flying safely. This is satellite GNC with demanding pointing and performance requirements, and you will see your work go from concept to orbit on a constellation that is launching and growing today. Amazon Leo will delight customers with reliable connectivity regardless of where they are. In this role, you will: - Take your GNC algorithms from concept all the way to orbit, and stay close to them through integration, test, and on-orbit operations - Solve hard precise-pointing, payload-cueing, and fleet-autonomy problems against demanding performance requirements - Work across the full lifecycle — design, simulation, test, and flight operations — rather than a narrow slice - Join a small team of some of the top GNC engineers in the industry, building a product people will love to use This position is part of the Satellite Attitude Determination and Control team, where we own the full lifecycle of our work — from algorithm design and flight software, through lab integration, to flying our own satellites on orbit. You will design and analyze the control system and algorithms, support development of our flight hardware and software, help integrate the satellite in our labs, and operate the GNC system in flight as a constellation of satellites flows through the production line into orbit. **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 - Design and analyze algorithms for state estimation, attitude determination and control, precise pointing, and payload cueing against demanding performance requirements. - Write the production C++ flight code that runs these algorithms on the satellite. Our GNC engineers own their algorithms all the way into flight software, working alongside software development engineers rather than handing prototypes off to be reimplemented by another team. - Design concepts of operation that balance pointing and payload cueing, momentum management, power, thermal, and communications across all phases of the mission, including off-nominal and contingency scenarios. - Develop momentum management strategies using reaction wheels and magnetic torquers, and help size and lay out the GNC sensor and actuator suite — star trackers, magnetometers, sun sensors, IMUs, and GPS receivers — and the algorithms that consume them. - Develop onboard autonomy, including Fault Detection, Isolation, and Recovery, so the fleet operates safely at scale with minimal intervention. - Develop and validate models and simulations of the satellite and constellation across a range of fidelity levels, including 6-DOF time-domain simulation and Monte Carlo analysis, and run hardware-in-the-loop testing to verify closed-loop performance. - Support component-level environmental testing, functional and performance checkout, and subsystem and satellite integration in our labs. - Analyze fleet telemetry in bulk and build the metrics, dashboards, and tooling that let us monitor and continuously improve pointing performance and payload operations time as the constellation grows. - Operate the GNC system on orbit — reviewing fleet data, supporting maneuvers, and resolving anomalies when on-orbit behavior diverges from the ground model. - Contribute to architecture, design, and code reviews, and document your algorithms, analyses, and results clearly. A day in the life This role spans a diverse set of problems across the spacecraft and network system. You will derive and prototype estimation and control algorithms, turn them into production flight code, and prove them out through 6-DOF and Monte Carlo simulation and hardware-in-the-loop testing in the lab. You will support satellite integration, and when something on orbit does not behave the way the ground model predicted, you will dig into fleet telemetry to find out why and drive the fix. You will own problems end to end and have the focus to go deep on each one. Because GNC sits at the center of safe flight, you will work across the whole program. The payload, power, thermal, and operations teams all depend on GNC, so you will balance their needs against your pointing and momentum budgets. To get your algorithms running reliably on the spacecraft, you will partner closely with avionics, flight software, and our embedded software development engineers. You will help the mechanical teams shape the next generation of satellite hardware, from reaction wheels to star trackers. And as satellites move from design through manufacturing into orbit, you will work alongside systems engineering and Assembly, Integration & Test. The problems are genuinely hard — demanding pointing and performance requirements, a large constellation to keep flying safely, and the chance to invent advanced capabilities that have not been built before. The work is staffed with some of the strongest engineers in the industry, and the constant collaboration across teams makes it a place to keep learning and growing. About the team Our team brings deep experience across many satellite systems and other flight vehicles, with real bench strength in both our mission and the core GNC disciplines. We design, prototype, test, iterate, and learn together, and because GNC is central to safe flight, we tend to drive Concepts of Operation and many of the system-level analyses on the program. We also have a lot of freedom. We trust our engineers to pursue the problems they face in whatever way they find best, to choose their own tools and approaches, and to own the results. If you want the autonomy to solve hard problems your way, and a team of strong engineers to do it with, you will feel at home here.

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 are strategically important to our businesses driving long term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Advertiser Growth Engine (AGE) team owns and builds services and applications across Amazon World-Wide Advertising that make advertising across multi-marketplaces as easy as flipping on a switch. We are focused on: (1) expanding Amazon Ads advertiser base, and (2) eliminating localization, operational, and marketplace knowledge gap barrier for Advertisers who advertise across multiple marketplaces. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth globally. We're looking for an experienced Applied Scientist with exceptional technical, analytical, and innovative capabilities to research, design, and create elegant machine learning solutions. The solutions will help our advertisers with multi-media and multi-lingual advertising offerings. You will use ideas from various domains of machine learning, including supervised and unsupervised methods, Deep Neural Networks, Natural Language Processing (NLP), and Computer Vision (CV) to build ML models that localizes multi-media advertising contents, including text, images and videos. You will also identify opportunities to leverage ML beyond localization, including, international expansion and global campaigns. Your work will directly impact our customers in the form of products and services used directly by our advertisers as well as our third-party integrators. As an Applied Scientist on this team, you will: - Build and deliver end-to-end machine learning solutions; build ML models and perform data analysis to deliver scalable solutions to business problems. - Perform hands-on analysis and modeling with enormous data sets to develop insights that increase traffic monetization and merchandise sales without compromising shopper experience. - Work closely with software engineers on detailed requirements to productionize the ML models you build. - Run A/B experiments that affect hundreds of millions of customers, evaluate the impact of your optimizations and communicate your results to various business stakeholders. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new innovate machine learning approaches. 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. Team video https://youtu.be/zD_6Lzw8raE

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Selling Partner Trust & Store Integrity Science Team. We are looking for a talented scientist who is passionate to build advanced machine learning systems that help manage the safety of millions of transactions every day and scale up our operation with automation. Key job responsibilities Innovate with the latest GenAI/LLM/VLM technology to build highly automated solutions for efficient fraud detection, risk evaluation and automated operations Design, develop and deploy end-to-end advance machine learning solutions with vision anf GenAi technologies in the Amazon production environment to create impactful business value Learn, explore and experiment with the latest machine learning advancements to create the best customer experience A day in the life You will be working within a dynamic, diverse, and supportive group of scientists who share your passion for innovation and excellence. You'll be working closely with business partners and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

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. Reinforcement learning is transforming our robotic intelligence, enabling autonomous behavior without human guidance. We are seeking a Senior AI Engineer with deep expertise in reinforcement learning and deep learning, including supervised and self-supervised learning with a focus on dexterous manipulation. Your role will involve leveraging both simulated and real-world data to address practical challenges in dynamic grasping, contact-rich manipulation, and object interaction. If you are passionate about advancing AI and developing innovative solutions, join us in shaping the future of intelligent robotics. Key job responsibilities Develop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation, translating vision, depth, tactile, and proprioceptive sensor input into precise end-effector and joint-level motor commands. Design, test, and refine algorithms to solve complex real-world manipulation challenges, such as handling diverse package form factors, dynamic hand-offs, and operating door handles or latches. Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data.