Careers

Amazon’s Middle Mile Science group is looking for a Senior Applied Scientist to build machine learning and optimization models to support pricing and revenue management of its external freight business. This includes the development of novel forecasting and dynamic pricing models, as well as the application of causal inference and artificial intelligence techniques, to improve marketplace services and execution for our customers. The Middle Mile Science group develops optimization and machine learning systems that power Amazon's freight transportation network, from network design and pricing to real-time load planning and capacity utilization. The scale of Amazon's fulfillment operations requires robust transportation networks that minimize cost while meeting all customer deadlines. Real-time execution depends on state-of-the-art optimization and artificial intelligence to coordinate thousands of operators and drivers. This includes shipper-facing and carrier-facing marketplace algorithms as well as network planning and optimization tools. Amazon often finds that existing techniques do not match our unique business needs,driving the innovation of new approaches and algorithms. As a Sr. Applied Scientist responsible for middle mile transportation, you will be working closely with different teams including business leaders and engineers to design and build scalable products operating across multiple transportation modes. You will create experiments and prototype implementations of new learning algorithms and prediction techniques. You will have exposure to top level leadership to present findings of your research. You will also work closely with other scientists and engineers to implement your models within our production system. You will implement solutions that are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility, and make decisions that affect the way we build and integrate algorithms across our product portfolio. About the team Our Middle Mile Marketplace Science team builds the algorithms for Amazon’s rapidly growing freight marketplace. Amazon contracts with 3P shippers and a network of independent carriers, using a mix of contract structures with varying service and risk profiles. Our work focuses on mechanisms and learning algorithms to optimize pricing and matching in this complex marketplace, and continually improve the experience for carriers and shippers. This is an area with many challenging problems and a huge business impact for Amazon!

Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazon's digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action? The Amazon Digital Acceleration Analytics team is looking for an analytical and technically skilled individual to join our team. In this role, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission This role offers wide scope, autonomy, and ownership. You will work closely with software engineers & data engineers to put algorithms into practice. You should have strong business judgement, excellent written and verbal communication skills. The candidate should be willing to take on challenging initiatives and be capable of working both independently and with others as a team. Key job responsibilities We are looking for an experienced data scientist with strong foundations in mathematics, statistics & machine learning with exceptional communication and leadership skills, and a proven track record of delivery. In this role, You will Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for engineering teams. Design and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. Drive end-to-end statistical analysis that have a high degree of ambiguity, scale, and complexity. Research and develop advanced Generative AI based solutions to solve diverse customer problems. About the team The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.

Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As an Applied Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning, and advanced clustering. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Invent and adapt new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Design and deploy production-quality ML components that directly impact advertiser trust and the business top-line. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Contribute to the scientific community through publications at peer-reviewed venues and reviewing research submissions. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)

Amazon Robotics is transforming warehouse automation through edge AI and machine learning applied to real-world robotics challenges. We're seeking a Applied Scientist to advance our mobile manipulation capabilities by developing learning-based approaches that enable robots to navigate and manipulate objects in dynamic fulfillment environments. This role offers the opportunity to apply state-of-the-art research to production systems operating at Amazon's unprecedented scale. Key job responsibilities - Model Development and Training: Designing and implementing the model architectures, training and fine tuning the models using various datasets, and optimize the model performance through iterative experiments - Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines. - Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance. - Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. 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 Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of enthusiastic doers that work passionately to apply innovative advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun!

We are seeking a Principal Applied Scientist to lead research and development in automated reasoning, formal verification, and program analysis. You will drive innovation in making formal methods practical and accessible for real-world systems at cloud scale. Key job responsibilities - Lead research initiatives in automated reasoning, formal verification, SMT solving, model checking, or program analysis - Design and implement novel algorithms and techniques that advance the state of the art - Mentor and guide applied scientists, research scientists, and engineers - Collaborate with product teams to transition research into production systems - Define technical vision and strategy for automated reasoning initiatives - Represent AWS in the academic and research community - Drive cross-organizational impact through technical leadership About the team The Automated Reasoning Group at AWS develops and applies cutting-edge formal methods and automated reasoning techniques to ensure the security, reliability, and correctness of AWS services and customer applications. Our work innovates tools and services to perform verification at scale and apply them to build safe and secure systems at AWS. We are also pioneering the use of formal verification and automated reasoning to develop agentic systems, ensuring AI agents operate within defined safety boundaries.

We are seeking a talented, customer-focused Senior Applied Scientist to join the AVS ProServe Science and Data Team. In this role, you will develop and apply machine learning algorithms to solve ambiguous business problems, leading the roadmap to help vendors understand their customers, identify opportunities for growth and define long-term strategy. The ideal candidate brings deep expertise in machine learning, interpretable models, transformers, and experimentation, along with the business acumen to translate problems into scalable science solutions. We're looking for a self-starter with an entrepreneurial spirit who is comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment — with a passion for driving measurable impact. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities • Develop customer understanding models by designing and building orchestrated ML solutions that enable vendors to deeply understand their customers and uncover growth opportunities • Bridge science and business strategy by translating model outputs into actionable insights and recommendations that inform vendor growth strategies, customer acquisition, and long-term planning • Measure and validate business impact by closing the loop between science solutions and business outcomes — establishing measurement frameworks that quantify impact, surface new opportunities, and continuously refine the path to vendor growth • Lead cross-functional collaboration by working with engineers, scientists, consultants, and business leaders to deploy scalable solutions while communicating complex technical concepts clearly to non-technical audiences • Stay at the forefront of innovation by applying state-of-the-art techniques in machine learning, interpretable models, and transformers to solve ambiguous business problems while fostering rapid experimentation and continuous learning

The Alexa AI, AURORA: Alexa Understanding, Runtime, ORchestration, and Applied sciences org is seeking a passionate, talented, and resourceful Senior Applied Scientist to invent and build scalable solutions for sate-of-the-art conversational AI. You will be working on advancing technologies in the fields of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP). As part of this role, you will collaborate with talented peers, have significant influence on our overall strategy as you guide and mentor Applied Scientists to create innovative and scalable solutions that touch millions of Alexa customers. Creating reliable, scalable, and high performance products requires exceptional technical expertise, a sound understanding of the fundamentals of AI, and practical experience building large-scale machine learning systems. The ideal candidate will be a self-starter who can dive into a project with limited guidance and is able to design and implement inventive, simple solutions to complex problems. They will be passionate about new technologies and have a track record of delivering valuable software features and products in a fast-paced, highly iterative environment. A commitment to team work, hustle, and strong communication skills (to both business and technical partners) are absolute requirements. Join us in our mission to shape the space of Generative AI and provide unparalleled experiences for our customers. Key job responsibilities • Define the science roadmap and advance core science primitives for conversation modelling, content generation, and automated quality assurance via state-of-the-art computer vision, machine learning, and generative AI • Architect agentic systems, making high-judgment trade-offs across audio/text/visual quality, relevance, latency, cost, and long-term extensibility • Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives, institutionalizing rigorous validation across customer touch points • Own end-to-end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal guidance • Identify whitespace opportunities by staying at the forefront of AI/ML advances and translating them into actionable research directions with clear customer and business impact • Drive development and deployment of scalable agentic systems for conversation understanding and generation, ensuring architectural decisions support long-term platform evolution • Set and continuously raise the scientific and engineering bar across the team • Tackle the team's most complex technical problems while maintaining practical focus on customer value and solution generalizability • Advance the team's scientific reputation through high-impact publications and presentations at top-tier internal and external venues, and generate intellectual property through patents About the team AURORA is the AI runtime backbone and horizontal intelligence team that powers Alexa's core infrastructure, AI capabilities, and specialized conversational models. We revolutionize conversational AI through three core pillars: architecting mission-critical AI runtime systems, advancing science solutions that connect key conversational capabilities, and transforming how builders create at scale. We empower 1P and 3P engineers and scientists worldwide with modular, reusable platforms that accelerate innovation while delivering accurate, responsive, and reliable conversational experiences to millions of end-users through operational excellence at scale.

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research! Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization. - Develop and test new signals for improving recommendation models - Use supervised and uplift learning algorithms to improve customer experience - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area - Present and publish science research, contributing to Amazon's science community - Mentor junior engineers and scientists. About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.

Do you want to make a real difference to real people's lives? Want to design and build fair and explainable systems which automate recruitment processes across Amazon? Come and be part of a team that develops new machine learning (ML) technologies, which help Amazon scale for its customers by recruiting diverse teams. Join our Recommendations team within Intelligent Talent Acquisition (ITA) where you’ll build machine learning products that transform how job seekers find opportunities and recruiters discover talent. You’ll develop sophisticated recommendation systems powering both Amazon Jobs and internal hiring platforms, operating at global scale to match the right people with the right positions. Using techniques including representation learning, reinforcement learning, and probabilistic modeling, your work will directly improve efficiency for recruiters and help candidates find their ideal roles. This position offers the chance to solve complex problems with significant impact by creating systems that make Amazon’s entire hiring ecosystem more effective while collaborating with scientists across the organization. Key job responsibilities - Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale. Our ideal candidate has a strong scientific foundation and experience of statistical analysis and model building and has a passion for fairness and explainability in ML systems. - Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience. - Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models. - Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability. - Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition. You will have the opportunity to provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve. A day in the life You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline. Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise. About the team The Recommendations team is a hybrid group of software engineers and applied scientists located in Edinburgh. We build tools that match people to jobs and jobs to people, optimizing experiences for both recruiters and candidates. Our work directly impacts Amazon’s ability to find and hire exceptional talent globally. The team maintains a collaborative environment with regular knowledge sharing and mentorship opportunities. We work closely with our product teams to understand business needs and develop innovative scientific solutions that improve hiring outcomes across both industry and student requisitions worldwide.

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.