Careers

Amazon is looking for a motivated individual with strong statistical, analytical skills and technological experience to join the DIGI (Data Infrastructure and Generative Intelligence) Ad Sales Finance analytics team. In this position the successful candidate will be responsible for partnering with Finance and Business leaders to optimize Sales Forecasts Key job responsibilities - Build and train models to support forecasting and planning for Advertising Sales Finance - Have strong technical experience, but also be able to work with non-tech partners and communicate complex and technical topics in a simple and understandable fashion - Have a good understanding of machine learning or statistical modeling techniques, including a strong understanding of model parameters and how they affect performance - Understand time-series forecasting techniques (e.g., STL decomposition, ETS/Holt-Winters, ARIMA, Prophet, or similar models) - Familiarity with hierarchical or segmented forecasting problems (e.g., product, region, channel, or customer-level splits) - Apply theoretical or statistical models in an applied, real-world environment - Perform model evaluation such as confidence intervals, error metrics, backtesting, and validation datasets - Work with large, complex datasets across multiple dimensions - Translate analytical findings into clear, actionable insights for business stakeholders A day in the life In this position the successful candidate will be responsible for partnering with Finance and Business leaders to expand and optimize forecasting models that supports weekly, monthly, quarterly and annual reviews for the Display Ads Finance group and our stakeholders. About the team The Advertising Sales Finance Analytics & FP&A team's responsibilities comprise of corporate reporting, planning, Headcount & OpEx, Goals reporting, and ad-hoc analysis. We support Advertising leaders and finance teams by coordinating and consolidating deliverables, centralizing and standardizing processes, establishing financial controls and mechanisms, building tools that improve the speed of decision making, and providing insightful financial analysis on the short and long term strategy of Advertising.

We are seeking a talented, customer-focused applied scientist to join our JCI Measurement and Optimization Science Team (JCI MOST), with a charter to build scalable systems that automatically detect pricing defects, implement intelligent corrections, measure intervention impacts, and deliver data-driven pricing strategies to leadership. This role requires an individual with exceptional machine learning, LLM, and Causal Inference expertise, strong system architecture capabilities, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit to drive measurable improvements in pricing quality and competitiveness. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment. Key job responsibilities Key Job Responsibilities • Build scalable defect detection systems that automatically identify pricing anomalies, competitive gaps, and quality issues across millions of products using ML and LLM models and real-time monitoring • Deploy automated defect remediation with intelligent pricing recommendations, and validation frameworks that reduce manual intervention requirements • Measure impact and drive strategy by establishing robust measurement frameworks, designing large-scale experiments, building attribution models, and developing executive dashboards that translate findings into actionable insights for leadership • Lead cross-functional collaboration by partnering with product, engineering, and science teams to deploy solutions at scale while communicating complex technical concepts clearly to executive audiences • Stay at the forefront of innovation by applying state-of-the-art techniques in ML, deep learning, LLM, and causal inference to pricing quality challenges while fostering rapid experimentation and continuous learning

This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scalable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.

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.

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). 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 visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision. Key job responsibilities As an Applied Science Manager, 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 Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. 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 create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.

Are you a Graduate Student interested in machine learning, natural language processing, computer vision, automated reasoning, robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Key job responsibilities Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. A day in the life Come teach us a few things, and we’ll teach you a few things as we navigate the most customer-centric company on Earth.

Amazon’s The Middle Mile Science group is looking for a Senior Applied Scientist specializing in design and evaluation of algorithms for predictive modeling and optimization applied to large-scale transportation planning systems. This includes the development of novel machine learning and artificial intelligence techniques to improve on marketplace optimization solutions. The Middle Mile Science group is charged with developing an evolving suite of optimization tools to facilitate the design of efficient air and ground transport networks, optimize the flow of packages within the network to efficiently align network capacity and shipment demand, set prices, and effectively utilize scarce resources, such as aircraft and trucks. The scale of Amazon’s fulfillment operations challenges us to design, build and operate robust transportation networks that minimize the overall operational cost while meeting all customer deadlines. Real-time execution of our network depends on state-of-the-art artificial intelligence tools to coordinate the actions of thousands of operators and drivers in real time. Amazon often finds existing techniques do not effectively match our unique business needs, which necessitates the innovation and development of new approaches and algorithms to find an adequate solution. 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!

At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research

Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our research builds on that of Amazon’s broader AGI organization, which recently introduced Amazon Nova, a new generation of state-of-the-art foundation models (FMs). Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up! Key job responsibilities You will be responsible for maintaining our task management system which supports many internal and external stakeholders and ensures we are able to continue adding orders of magnitude more data and reliability.

Selection Monitoring team is responsible for making the biggest catalog on the planet even bigger. In order to drive expansion of the Amazon catalog, we develop advanced ML/AI technologies to process billions of products and algorithmically find products not already sold on Amazon. We work with structured, semi-structured and Visually Rich Documents using deep learning, NLP and image processing. The role demands a high-performing and flexible candidate who can take responsibility for success of the system and drive solutions from research, prototype, design, coding and deployment. We are looking for Applied Scientists to tackle challenging problems in the areas of Information Extraction, Efficient crawling at internet scale, developing models for website comprehension and agents to take multi-step decisions. You should have depth and breadth of knowledge in text mining, information extraction from Visually Rich Documents, semi structured data (HTML) and advanced machine learning. You should also have programming and design skills to manipulate Semi-Structured and unstructured data and systems that work at internet scale. You will encounter many challenges, including: - Scale (build models to handle billions of pages), - Accuracy (requirements for precision and recall) - Speed (generate predictions for millions of new or changed pages with low latency) - Diversity (models need to work across different languages, market places and data sources) You will help us to - Build a scalable system which can algorithmically extract information information from world wide web. - Intelligently cluster web pages, segment and classify regions, extract relevant information and structure the data available on semi-structured web. - Build systems that will use existing Knowledge Base to perform open information extraction at scale from visually rich documents. Key job responsibilities: - Use AI, NLP and advances in LLMs/SLMs and agentic systems to create scalable solutions for business problems. - Efficiently Crawl web, Automate extraction of relevant information from large amounts of Visually Rich Documents and optimize key processes. - Design, develop, evaluate and deploy, innovative and highly scalable ML models. - Work closely with software engineering teams to drive real-time model implementations. - Establish scalable, efficient, automated processes for large scale model development, model validation and model maintenance. - Lead projects and mentor other scientists, engineers in the use of ML techniques. - Publish innovation in research forums. Basic qualifications: - 3+ years experience building ML models for business application. - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience. - Experience programming in Python, Java, C++ or related languages. - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Preferred qualifications: - Experience using Unix/Linux. - Experience in professional software development. - Experience in patents or publications at top-tier peer-reviewed conferences or journals. - Master's degree or above in computer science, computer engineering, or related field.