Careers

Do you ever struggle to explain your work? How's this: "Do you shop at Amazon? Do you know that box that says 'Add to Cart' and shows a price? Our team owns the model which picks that offer and the customer experience around the display of the offer price and the elements surrounding the 'Add to Cart' button which inform a purchase decision. We pick and display offers several billion times a day across all surfaces (mobile app, mobile web, desktop, Alexa shopping) worldwide. Our mission is to be the world’s first and most trusted choice for every customer on earth to discover and evaluate any product or service. Our team blends machine learning models to rank and select the best offer from the most trusted merchant for all products sold on Amazon along with a world-class front-end user experience for offer comparison to our global customers. We are responsible for the experiences and services that enable developers, including our own, to create tailored shopping experiences for every customer, product, business and marketplace offered by Amazon. We build scalable and extensible frameworks which allow for many teams at Amazon to innovate within the Offers Experience in a federated manner. If you are passionate about influencing and delivering the next-generation Amazon customer buying experience, we want to meet you. We are looking for a Senior Applied Scientist to join one of the most impactful and visible teams in Amazon. In this role you will work with business stakeholders throughout Amazon and provide technical direction and business expertise to strategize and help launch new businesses and features for our customers. You will use data to justify customer friendly decisions and present customer and financial impact of the changes we make to our stakeholders. You will work closely with other scientists, engineers, product managers, TPMs, managers, and senior leadership team to understand and drive business impact. Successful candidates will have experience working on multiple projects with different stakeholders, make data driven decisions, have strong communication skills to interact with executives, non-technical and technical individuals and have a high technical bar along with a passion for people and project management. This is an opportunity to work with a team that drives one of the most coveted real estate in the E-commerce, the Amazon ‘Buy Box’ on Amazon Product Detail Page, Amazon Search Page , multiple buying widgets etc. on the Amazon desktop, mobile and tablet environments. Key job responsibilities * Build, innovate and maintain FMA's key offer selection algorithms * Collaborate with peer scientists and partner organizations to align on strategic algorithmic inputs * Research and deliver innovative techniques for ranking, simulation and evaluation systems * Build and maintain AI, ML and LLM integrations

As an Applied Scientist, you will be responsible for bringing new product designs through to manufacturing. You will work closely with multi-disciplinary groups including Product Design, Industrial Design, Hardware Engineering, and Operations, to drive key aspects of engineering of consumer electronics products. In this role, you will use expertise in physical sciences, theoretical, numerical or empirical techniques to create scalable models representing response of physical systems or devices, including: * Applying domain scientific expertise towards developing innovative analysis and tests to study viability of new materials, designs or processes * Working closely with engineering teams to drive validation, optimization and implementation of hardware design or software algorithmic solutions to improve product and customer risks * Establishing scalable, efficient, automated processes to handle large scale design and data analysis * Conducting research into use conditions, materials and analysis techniques * Tracking general business activity including device health in field and providing clear, compelling reports to management on a regular basis * Developing, implementing guidelines to continually optimize design processes * Using simulation tools like LS-DYNA, and Abaqus for analysis and optimization of product design * Using of programming languages like Python and Matlab for analytical/statistical analyses and automation * Demonstrating strong understanding across multiple physical science domains, e.g. structural, thermal, fluid dynamics, and materials * Developing, analyzing and testing structural solutions from concept design, feature development, product architecture, through system validation * Supporting product development and optimization through application of analysis and testing of complex electronic assemblies using advanced simulation and experimentation tools and techniques

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.

Are you a scientist passionate about advancing Information Retrieval, NLP, and Large Language Models? Do you want access to massive datasets, world-class compute, and a team of top scientists and engineers building the future of e-commerce? If so, you'll be a great fit for our team at Amazon. We build large-scale ML solutions that deliver personalized, up-to-date recommendations to millions of customers. Our team is uniquely positioned to shape how customers think about their shopping journey. We're looking for scientists with deep LLM expertise to build our next generation of models. The team focuses on post-training—instruction tuning, reward modeling, reinforcement learning, and multi-modal alignment. You'll design and run large-scale experiments, analyze model behavior, and develop training recipes that improve core capabilities like reasoning, personalization, and other frontier paradigms. Key job responsibilities Own the scientific roadmap for personalization initiatives, identifying high-impact research directions and translating ambiguous business problems into well-defined ML formulations Design and lead end-to-end systems spanning recommendations, information retrieval, and LLM fine-tuning, from problem framing through offline experimentation to production A/B testing and launch Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact and operational constraints Mentor and raise the bar for the science team through design reviews, paper discussions, and establishing best practices for experimentation and reproducibility Influence cross-functional strategy by partnering with engineering, product, and leadership to define the product vision informed by what's technically feasible and scientifically novel Publish and advance the state of the art — contribute to the broader ML community through patents, publications, and external engagement at conferences A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people who are passionate about making a meaningful impact on communities and the environment while helping shape the future of sustainable business practices. The Worldwide Sustainability (WWS) organization capitalizes on Amazon's scale and speed to build a more resilient and sustainable company. We manage our social and environmental impacts globally and drive solutions that enable our customers, businesses, and the world to become more sustainable. Through innovative programs and strategic partnerships, we're creating lasting positive change in the communities where we operate while advancing Amazon's commitment to environmental stewardship and social responsibility. We are looking for a robotics scientist to build and operate the first autonomous materials discovery laboratory at Amazon. This role combines deep robotics expertise (motion planning, control, platform integration) with modern Physical AI approaches (vision-language-action models, sim-to-real transfer, agentic orchestration). You will design autonomous experimental workflows that integrate dexterous robotic platforms, analytical instruments, and AI-driven hypothesis generation into a closed-loop discovery pipeline — where foundation models drive hypothesis generation and experimental planning, validated on real hardware under real chemistry. This is not a pure research role. You will work directly with physical robots, laboratory instruments, and deployment pipelines. The work is expected to be published, but the primary measure of success is a working autonomous platform that generates scientific results. Materials science expertise is not required — the team includes domain scientists. What matters is strong AI and robotics foundations, scientific curiosity, and the drive to ship. Key job responsibilities - Develop, train, and benchmark robotic manipulation policies for materials synthesis and characterization using modern policy architectures (VLA architectures, diffusion policies). - Design and execute sim-to-real transfer strategies including domain randomization, physics parameter tuning, and visual domain adaptation for laboratory robotic systems. - Integrate robotic platforms and laboratory instruments into automated workflows via APIs (SiLA 2, or equivalent), building real-time data pipelines for multimodal experimental outputs. - Architect policy training pipelines combining teleoperation data, synthetic demonstrations, reinforcement learning, and imitation learning for dexterous lab manipulation. - Build production-grade agentic runtime systems — failure detection, retry logic, exception handling, and human-handoff protocols — for unattended experimental sessions. - Design and execute autonomous experimental campaigns applying active learning, Bayesian optimization, or RL to drive iterative materials discovery. - Drive technical design reviews and set scientific direction for the autonomous lab platform. A day in the life You build the Physical AI systems that power robotics in autonomous science lab, one where foundation models generate hypotheses, robots execute experiments, and closed-loop optimization discovers materials that did not exist yesterday. You train manipulation policies in simulation, transfer them to a physical cobot, and watch real chemistry validate (or invalidate) an AI-generated theory. The signal here is not a metric on a dashboard; it is a synthesizing and testing novel material with measurable sustainability impact. If you want your research to have physical weight, this is the lab. About the team Sustainability Science and Innovation (SSI) is a multi-disciplinary research team within WW Sustainability combining science, ML, economics, and engineering. The autonomous laboratory is a new capability being built from the ground up. You will work alongside computational materials scientists, chemists, and ML engineers — with access to AWS-scale compute and Amazon's supply chain for hardware. The work targets sustainability outcomes across packaging, building materials, and alternative fuels.

You will build and lead the economics research agenda for measurement, experimentation, and value attribution for Amazon's Devices & Services organization. Your team is the "truth layer" of the Intelligence Core — the shared economics and causal inference capability that serves all Devices product lines, marketing pods, and Finance leadership with causal evidence of what Devices are worth and whether our investments are working. This is not a traditional analytics or measurement role. You will own an active research program in experimentation design — identifying and executing the causal studies that produce the causal inputs for pricing decisions, marketing optimization, and portfolio strategy. Your outputs provide the causal evidence base that L8 peers and senior leadership consume to make billions of dollars in investment decisions across the D&S portfolio. You will also own the economic models that validate and drive execution across the full surface area of marketing spend for devices and services. Key job responsibilities Economic Value: • Downstream value attribution for all Devices product lines — Impact on Prime, subscription lift, consumer spending, advertising value • Alexa+ value isolation and cross-PL attribution • Causal frameworks connecting device sales to Prime acquisition, subscription retention, and ecosystem engagement Marketing Science & Measurement: • Build the marketing science function from scratch • Incrementality measurement for marketing spend across all channels • Attribution methodology, measurement standards, and cross-pod governance • Marketing ROI frameworks for use by category marketers • CCM certification methodology and scenario planning models for optimal investment allocation Experimentation: • Owning the estimation methodology, identification strategies, data inputs/outputs, and refresh cadence • You will build this team's analytics function with AI at its core from day one • Experimentation governance — managing interference across teams, setting standards for causal validity • Evaluation framework for AI agents and autonomous optimization systems

Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Sr Data Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As a Data Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.

Amazon Japan is seeking a Data Scientist to join our Cost-to-Serve Intelligence team — a group that answers the question: "Why does it cost what it costs to deliver a package, and how do we do it more efficiently?" You will design and run research studies that connect operational data to business decisions, helping leadership understand where to invest to reduce cost-to-serve across Japan's logistics network — ultimately enabling faster, cheaper delivery that improves the customer experience. 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. This is a science role with direct business impact. Your work will be presented to senior executives, sized in dollar terms, and used to prioritize multi-million-dollar operational investments. The cost savings you identify flow back to customers through lower prices and faster delivery. If you enjoy turning complex data into clear recommendations that people act on, this is the role. Key job responsibilities - Design and execute quantitative studies that explain why cost-to-serve moves — isolating root causes from noise and quantifying improvement opportunities - Bridge science to business decisions: translate statistical findings into investment recommendations, opportunity sizing, and initiative prioritization that leadership can act on - Partner cross-functionally with operations, finance, supply chain, and product teams to define research questions, validate findings, and ensure insights drive real-world action - Own the full research lifecycle — from problem framing and data exploration through methodology design, analysis, and stakeholder-ready deliverables - Apply a range of scientific methods (econometrics, statistical modeling, machine learning, AI-assisted analysis) matched to the problem at hand - Communicate findings effectively to both technical and non-technical audiences through structured documents, presentations, and data visualizations - Continuously improve the team's analytical toolkit — introducing new methods, automating repetitive analysis, and raising the bar on scientific rigor A day in the life You might start the morning in a sync with your Applied Scientist partner, reviewing outputs from a model that estimates how different operational levers impact cost-to-serve. Mid-morning, you join a working session with a partner team in supply chain or finance, aligning on what questions your next study should answer and what data you'll need. After lunch, you're building and validating a quantitative model — using Python, SQL, and AI-powered tools to test causal hypotheses, estimate coefficients, and ensure the model delivers reliable insights at scale. You then structure your findings into an actionable recommendation — quantifying the opportunity and proposing where to double down. You close the day preparing materials for a leadership review, translating your model outputs into a narrative that drives decisions. Your stakeholders span supply chain, operations, finance, and product. You are the person leaders come to when they need to understand why something changed, how much it matters, and what to do next. About the team We are a multi-disciplinary team of ~10 people — data scientists, applied scientists, product managers, and data engineers — who own Cost-to-Serve intelligence end-to-end: from the business questions through product strategy to the technical systems that deliver answers. We sit within JP Consumer Innovation and operate at the intersection of multiple organizations (operations, finance, supply chain, technology), giving us broad visibility and outsized influence on Amazon Japan's P&L. The work is high-visibility and high-impact. Our insights and products are consumed by VP-level executives and directly shape Japan-wide investment priorities. When we find a way to reduce cost-to-serve, that efficiency flows through to customers as faster delivery and better prices — the virtuous cycle at the heart of Amazon's flywheel. The culture is intellectually rigorous but collaborative — we publish internal science papers, present at company-wide summits, and run cross-functional knowledge-sharing sessions with hundreds of attendees. We value clear thinking over title, and mechanism over assertion. We are based in Tokyo and operate bilingually (English/Japanese).

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. As for Brand Stores (e.g., amazon.com/lego and 1MM+ more), we are the exclusive destination for brand owners to showcase their content and product catalog through custom creative, seamlessly integrated with Amazon Stores and ads products, attracting millions of daily visits. We're reinventing how brands and shoppers connect. Using generative AI, we're building the next generation of brand-centric storefronts, reimagining store creation, optimization, performance analysis, and customer insights through state-of-the-art GenAI technologies. As a Senior Applied Scientist on the team, you'll own the science strategy and hands-on development of AI-powered brand experiences at Amazon scale. You'll operate at the intersection of frontier research and high-impact production systems, with the autonomy to shape what we build, how we build it, and where we invest next. This role combines science leadership, technical depth, product intuition, and business acumen. #GenAI Key job responsibilities If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, this is the role. * Build intelligent systems for Brand Stores. Develop AI-powered solutions leveraging generative models to optimize both advertiser and shopper experiences, measurably improving Brand Store performance. * Define a multi-year science vision and roadmap. Translate customer needs into actionable plans for applied scientists and engineering teams, blending science leadership, technical depth, product intuition, and business acumen. * Architect ahead of the GenAI curve. Anticipate where generative AI is heading over a multi-year horizon and position solutions to capitalize on compounding advances. * Experiment rigorously. Design and run A/B experiments grounded in deep data analysis to validate hypotheses and quantify impact. * Communicate with clarity. Translate complex technical ideas into compelling narratives for both technical and non-technical audiences. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Brand Stores team within Sponsored Products and Brands is chartered to create agentic brand store building experience, automating brand store creation, personalization, and optimization across global marketplaces, serving millions of brand owners world-wide.