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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

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.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will independently file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.

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.

The Workplace Health and Safety Central Science (WHS-CS) team researches factors that drive safety outcomes and identifies effective improvement programs. We partner with business teams on scientific problems, use data strategically for decision-making, and help bring a scientific lens to WHS as we work to make Amazon Earth’s safest employer in the industries in which it operates. A successful candidate will be able to partner effectively with both business and technical teams, including clear communication of results and the ability to influence a variety of stakeholders. The ideal candidate will have experience applying causal inference techniques and impact estimation, and be curious and excited to apply these in challenging real-world settings. They will thrive in working to deliver impactful solutions to the business problem, in the face of ambiguity over which modeling approaches will deliver the best results. Key job responsibilities Key job responsibilities -Work with engineers and data scientists on large-scale data modeling -Combine strong economic expertise with interdisciplinary learning -Execute big ideas as part of a technical team -Apply expertise in causal modeling and machine learning to measure the impact of key initiatives on safety outcomes. -Develop and maintain attribution models to understand the key drivers of safety performance. -Design experiments to measure pilot programs and support the scaling of successful experiments. -Collaborate with business stakeholders, product managers, and executive-level decision makers to synthesize findings into actionable insights. Successful candidates combine strong economic expertise with interdisciplinary learning, work effectively in diverse teams, and partner with engineering to develop scalable data resources that transform successful models into new products and services. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to helping them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Safest Employer for the industries in which it operates. Our charter is to deliver technology solutions and data insights that help reduce workplace risks and injuries at Amazon. Our customers are Worldwide (WW) Operations and the WHS organization. We conduct scientific research and modeling to generate actionable safety insights and provide analytical solutions.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

Amazon Autonomy is a research lab based in San Francisco with the charter to develop state of the art browser use agents at scale. Our research leverages Large Language Models (LLMS) and reinforcement learning (RL) algorithm at scale and benefits from the ability to work Amazon’s scale and a startup’s agility. As a Member of Technical staff (MTS) on the gym pod, you’ll be responsible for helping develop the training environments, tasks, and integrations needed to scale our data and drive core model capabilities. You’ll work closely with our researchers and given the autonomy to drive significant outcomes. This role is for you if you have a learner’s mindset, enjoy building at the frontier of what’s possible, and thrive in a collaborative fast-paced environment. Key job responsibilities Responsible for developing approaches to scale our RL environment from hundreds of web applications and tens of thousands of tasks to thousands of web applications and hundreds of thousands of tasks. Core Engineering: Architect and deliver robust software solutions throughout the development lifecycle Develop agentic harnesses that run autonomously with high accuracy Engineer and optimize high-performance systems at scale with modern TypeScript and Python Collaboration & Leadership: Collaborate with cross-functional stakeholders to deliver aligned features Mentor junior engineering staff Maintain technical documentation and architectural decision records

Join us in building the future of shopping! If you're passionate about creating unique mobile experiences that is an AI first experience. We are seeking exceptional applied scientists who will help build magical shopping experiences leveraging and extending state of the art machine learning that leverage foundational models and proved optimization techniques. You will be working on an inter-disciplinary team of highly-capable software engineers (mobile and backend), scientists, designers, and product managers to invent new experiences that work for millions of customers at scale. You will act as a technical lead guiding software development engineers and scientists to bring scientific improvements into production. Key job responsibilities • Lead a cross-functional team of applied scientists, machine learning engineers, and software developers in designing and implementing AI-powered shopping experiences using foundational models and advanced optimization techniques • Define technical strategy and roadmap for machine learning initiatives, making executive decisions on architecture, technology stack, and resource allocation to achieve business objectives at scale • Oversee the end-to-end development lifecycle, directing team members in the design, implementation, and deployment of innovative AI/ML solutions that serve millions of customers • Development and mentors engineers and scientists and advise on team composition and structure • Drive cross-functional collaboration at the leadership level, working with senior product managers, designers, and engineering leaders to align ML initiatives with broader organizational goals • Establish and enforce technical standards, code quality guidelines, and best practices across the organization, ensuring scalability, performance, and reliability of production systems • Make critical business decisions regarding project prioritization, and trade-offs between innovation speed and system stability • Represent the technical organization to executive leadership, providing strategic recommendations and reporting on team performance, project milestones, and resource needs

Prime Video is one of the world's fastest-growing entertainment destinations, with Live Sports at the center of that growth. From NFL and NBA to Premier League and Champions League, Prime Video is becoming a premier home for live sports globally. Our team owns the data science that shapes how tens of millions of customers discover and experience Sports content on Prime Video. We build measurement frameworks, experimentation systems, and analytical foundations powering Search, Recommendations, and the live broadcast experience itself. Sports presents uniquely exciting challenges: content is live and time-sensitive, customer intent shifts rapidly, and the difference between a great and poor experience is measured in minutes. We tackle problems like real-time search intent during live matches, sports engagement and long-term retention, broadcast optimization for global audiences, and personalization across vastly different affinities, from die-hard fans to first-time viewers. You'll work on large-scale experimentation, novel measurement methodologies, and data-driven decisions that directly shape the Prime Video customer experience. This is a ground-floor opportunity to define the data science practice for an expanding domain, from content discovery through the live viewing experience, partnering with Applied Scientists, Software Engineers, Product Managers, and Broadcasting Teams to turn insights into global product impact. Key job responsibilities - Experimentation at scale: Design, execute, and analyze A/B tests, from short-cycle learning experiments to full product launches across worldwide marketplaces - Measurement & metrics: Build frameworks to measure content discovery quality, search recall, recommendation relevance, and broadcast experience, including novel methodologies for live, time-sensitive content - Sports analytics: Deep-dive into customer behavior around live events: engagement patterns, affinity segmentation, broadcast quality, and tentpole event dynamics - Product partnership: Partner with Applied Scientists, Engineers, and Product Managers to define requirements, evaluate models, and drive data-informed decisions - Analytical leadership: Own data structures, metrics definitions, and best practices. Communicate findings clearly to technical and business stakeholders - Shape a growing domain: Help define the data science roadmap as we expand into new areas of live sports Please note, you will need strong SQL to perform this role.