Careers

Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Science Manager, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

This role is to solve business problems in Machine Learning for the Seller and Fulfilment Tech (SFT) org. The overarching goal of the team is to enhance ML expertise and fluency within SFT and across IST, championing engineering and operational excellence in ML model development and other related parts of the ML model lifecycle. Some of the key areas which the team owns in this space area: Selection Recommendations, Registration improvements, Bad actor detection and prevention Selection economics, Inventory recommendation, Delivery Promise Predictions, Seller success. Within the ML space, the scientist would have to solve intrinsically hard problems where neither problem nor solution is well defined. So, the leader should have high focus on building a deep understanding of the ML science space, experimentation methodology, as well as a high focus on embracing external trends, especially applications of GenerativeAI and LLMs. A large focus area for the role is to also contribute towards the science and research aspects. This role applies and extends existing scientific techniques, and invents new ones to address specific customers’ needs or business problems, at a project level. This should also lead to regular contributions to internal or external peer-reviewed publications that validate novelty

Amazon Aurora DSQL is a serverless, distributed SQL database with virtually unlimited scale, highest availability, and zero infrastructure management. Aurora DSQL provides active-active high availability, providing strong data consistency designed for 99.99% single-Region and 99.999% multi-Region availability. Aurora DSQL automatically manages and scales system resources, so you don't have to worry about maintenance downtime and provisioning, patching, or upgrading infrastructure. As a Senior Applied Scientist, you will be expected to lead research and development in advanced query optimization techniques for distributed sql services. You will innovate in the query planning and execution layer to help Aurora DSQL succeed at delivering high performance for complex OLTP workloads. You will develop novel approaches to stats collection, query planning, execution and optimization. You will drive industry leading research, publish your research and help convert your research into implementations to make Aurora DSQL the fastest sql database for OLTP workloads. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Key job responsibilities Our engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You’ll bring a passion for innovation, data, search, analytics, and distributed systems. You’ll also: Solve challenging technical problems, often ones not solved before, at every layer of the stack. Design, implement, test, deploy and maintain innovative software solutions to transform service performance, durability, cost, and security. Build high-quality, highly available, always-on products. Research implementations that deliver the best possible experiences for customers. A day in the life As you design and code solutions to help our team drive efficiencies in software architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also: Build high-impact solutions to deliver to our large customer base. Participate in design discussions, code review, and communicate with internal and external stakeholders. Work cross-functionally to help drive business decisions with your technical input. Work in a startup-like development environment, where you’re always working on the most important stuff. About the team Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. 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. About 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 empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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. 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.

The Region Flexibility Engineering (RFE) team builds and leverages foundational infrastructure capabilities, tools, and datasets needed to support the rapid global expansion of Amazon's SOA infrastructure. Our team focuses on robust and scalable architecture patterns and engineering best practices, driving adoption of ever-evolving and AWS technologies. RFE is looking for a passionate, results-oriented, inventive Data Scientist to refine and execute experiments towards our grand vision, influence and implement technical solutions for regional placement automation, cross-region libraries, and tooling useful for teams across Amazon. As a Data Scientist in Region Flexibility, you will work to enable Amazon businesses to leverage new AWS regions and improve the efficiency and scale of our business. Our project spans across all of Amazon Stores, Digital and Others (SDO) Businesses and we work closely with AWS teams to advise them on SDO requirements. As innovators who embrace new technology, you will be empowered to choose the right highly scalable and available technology to solve complex problems and will directly influence product design. The end-state architecture will enable services to break region coupling while retaining the ability to keep critical business functions within a region. This architecture will improve customer latency through local affinity to compute resources and reduce the blast radius in case of region failures. We leverage off the sciences of data, information processing, machine learning, and generative AI to improve user experience, automation, service resilience, and operational efficiency. Key job responsibilities As an RFE Data Scientist, you will work closely with product and technical leaders throughout Amazon and will be responsible for influencing technical decisions and building data-driven automation capabilities in areas of development/modeling that you identify as critical future region flexibility offerings. You will identify both enablers and blockers of adoption for region flex, and build models to raise the bar in terms of understanding questions related to data set and service relationships and predict the impact of region changes and provide offerings to mitigate that impact. About the team The Regional Flexibility Engineering (RFE) organization supports the rapid global expansion of Amazon's infrastructure. Our projects support Amazon businesses like Stores, Alexa, Kindle, and Prime Video. We drive adoption of ever-evolving and AWS and non-AWS technologies, and work closely with AWS teams to improve AWS public offerings. Our organization focuses on robust and scalable solutions, simple to use, and delivered with engineering best practices. We leverage and build foundational infrastructure capabilities, tools, and datasets that enable Amazon teams to delight our customers. With millions of people using Amazon’s products every day, we appreciate the importance of making our solutions “just work”.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Key job responsibilities * A Data Science Manager serving this role will act as thought partner to L8s of Product and Engineering organizations * Manage team of BIEs and Data Scientists leading strategic growth areas: retention, voice + visual engagement, organization-wide experimentation, Gen AI initiatives for customer sentiment analysis, and product excellence. * Bridge science, analytics, and engineering to deliver solutions informing strategic decisions on AM technology investments and long-term business performance * Lead experimentation program evolution, including culture enhancement, measurement efficiency tooling, and ROI tracking for AM leadership * Build and maintain customer segmentation models to understand streaming behaviors and forecast responses to personalization changes * Develop structural and predictive models using data science workflows to deliver measurable customer results A day in the life * Manage and develop data scientists specializing in experimentation and customer analytics * Lead strategic relationships across music voice + visual, retention, experimentation, Gen AI, and FPE initiatives * Conduct experimentation and hypothesis testing, build launch measurements, goal benchmarking, and forecasting * Develop science models for targeting and create operational reporting with anomaly detection for product health monitoring * Partner with product managers to translate business questions into actionable science projects * Influence product roadmaps through data-driven insights on customer behavior patterns * Provide hands-on leadership through daily stand-ups and sprint planning * Review code, data warehouse designs, and oversee ETL pipeline development * Monitor system performance and data quality metrics * Conduct stakeholder meetings and present insights to senior leadership * Design, build, and maintain real-time metrics, reports, analyses, and dashboards with full audit capabilities * Build interactive dashboards and reports using Amazon QuickSight * Identify and resolve customer experience friction points with cross-functional teams * Establish performance metrics and audit mechanisms to track team progress * Drive adoption of best practices and innovative solutions across the organization About the team The Data, Insights, Science and Optimization, Music Product & Technology (DISCO MPT) team is looking for a Manager Data Science to lead a team of Data Scientists, and Business Intelligence Engineers who analyze big data, provide analytics and insights as well as build models and build critical tools for experimentation. The DISCO MPT team enables the Music Product Technology organization to make data driven decisions that improve customer retention, engagement and experience on Amazon Music. We build and maintain automated self-service data solutions, data science models and deep dive difficult questions that provide actionable insights. We also enable measurement, personalization and experimentation by operating key data programs ranging from attribution pipelines to causal frameworks. In this role, you will set the vision and direction for the team and collaborate with internal stakeholders across marketing, growth, product, science and finance teams to scale and advance our science and analytics offerings.

We are looking for customer obsessed, highly analytical Data Scientist to lead the science innovation, transformation and development for evolving Amazon Fulfilment Tech Inventory, to identify and manage inventory defects and risks at e-commerce sale.. Our solutions and products are directly consumed by business teams and affect the lives of millions of end customers with availability, fast delivery speed and lower product costs. You will work with multiple partner teams across International Stores and Operations, and also WW partners. We are a team that thrives on growth, both personal and professional. This is the ideal role if you are excited about leveraging science for tangible business impact and have already some experience in the delivery experience and customer experience domains. Key job responsibilities - Lead the analysis, prototyping and implementation of solutions for Inventory Risk and Defect Management - Work closely with other scientists and engineers to review and improve your model design proposals - Partner with product managers and other business stakeholders, documenting and explaining your progress in business reviews, and being the technical voice in charge of your product - Lead data transformation in the business - identify and implement opportunities to simplify and sharpen the way we do business through data automation and analytics - Be active in the community, participating in science education/growth activities - Keep up to date with scientific development in the field

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities - Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Drive technical direction for specific research initiatives, ensuring robust performance in production environments. A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Collaborate with fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Collaborate with fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Contribute to focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Make significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, time-series forecasting, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the research frontier of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, time-series, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep-training in one area of econometrics. For example, many applications on the team use structural econometrics, machine-learning, and time-series forecasting. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members.

We are seeking a Senior Manager, Applied Science to lead the applied science charter for Amazon’s Last-Hundred-Yard automation initiative, developing the algorithms, models, and learning systems that enable safe, reliable, and scalable autonomous delivery from vehicle to customer doorstep. This role owns the scientific direction across perception, localization, prediction, planning, learning-based controls, human-robot interaction (HRI), and data-driven autonomy validation, operating in complex, unstructured real-world environments. The Senior Manager will build and lead a high-performing team of applied scientists, set the technical vision and research-to-production roadmap, and ensure tight integration between science, engineering, simulation, and operations. This leader is responsible for translating ambiguous real-world delivery problems into rigorous modeling approaches, measurable autonomy improvements, and production-ready solutions that scale across cities, terrains, weather conditions, and customer scenarios. Success in this role requires deep expertise in machine learning and robotics, strong people leadership, and the ability to balance long-term scientific innovation with near-term delivery milestones. The Senior Manager will play a critical role in defining how Amazon applies science to unlock autonomous last-mile delivery at scale, while maintaining the highest bars for safety, customer trust, and operational performance. Key job responsibilities Set and own the applied science vision and roadmap for last-hundred-yard automation, spanning perception, localization, prediction, planning, learning-based controls, and HRI. Build, lead, and develop a high-performing applied science organization, including hiring, mentoring, performance management, and technical bar-raising. Drive the end-to-end science lifecycle from problem formulation and data strategy to model development, evaluation, deployment, and iteration in production. Partner closely with autonomy engineering to translate scientific advances into scalable, production-ready autonomy behaviors. Define and own scientific success metrics (e.g., autonomy performance, safety indicators, scenario coverage, intervention reduction) and ensure measurable impact. Lead the development of learning-driven autonomy using real-world data, simulation, and offline/online evaluation frameworks. Establish principled approaches for generalization across environments, including weather, terrain, lighting, customer properties, and interaction scenarios. Drive alignment between real-world operations and simulation, ensuring tight feedback loops for data collection and model validation. Influence safety strategy and validation by defining scientific evidence required for autonomy readiness and scale. Represent applied science in executive reviews, articulating trade-offs, risks, and long-term innovation paths.