Careers

Do you want to leverage your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Core Science team would love to discuss how you can make that a reality. Our team is an interdisciplinary team that uses economics, behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world. In this role, you will spearhead science design and technical implementation innovations across our predictive modeling and forecasting work-streams. You'll enhance existing models and create new ones, empowering leaders throughout Amazon to make data-driven business decisions. You'll collaborate with scientists and engineers to deliver solutions while working closely with business stakeholders to address their specific needs. Your work will span various business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), utilizing a range of modeling approaches (linear, tree-based, deep neural networks, and LLM-based). You'll develop end-to-end ML solutions from problem formulation to deployment, maintaining high scientific standards and technical excellence throughout the process. As an Applied Scientist, you'll also contribute to the team's science strategy, keeping pace with emerging AI/ML trends. You'll mentor junior scientists, fostering their growth by identifying high-impact opportunities. Your guidance will span different analysis levels and modeling approaches, enabling stakeholders to make informed, strategic decisions. If you excel at building advanced scientific solutions and are passionate about developing technologies that drive organizational change in the AI era, join us as we work hard, have fun, and make history. Key job responsibilities • Model Development & Innovation: Design and implement novel GenAI/LLM solutions using foundation models (e.g., Claude, GPT) and AWS services including Amazon Bedrock, SageMaker, and other AWS AI/ML tools • Research & Experimentation: Conduct applied research to advance the state-of-the-art in LLM applications, including prompt engineering, few-shot learning, fine-tuning, and model evaluation • Production Deployment: Build scalable, production-ready AI systems that serve millions of requests with high reliability, low latency, and cost efficiency • Cross-Functional Collaboration: Partner with product managers, engineers, and business stakeholders to translate business requirements into technical solutions and drive measurable impact • Technical Leadership: Mentor junior scientists, contribute to technical strategy, and establish best practices for GenAI development across the organization • Evaluation & Metrics: Design rigorous evaluation frameworks to measure model performance, bias, safety, and business impact • Documentation & Communication: Publish technical papers, create detailed documentation, and present findings to both technical and non-technical audiences About the team The People eXperience and Technology (PXT) Core Science Team uses science, engineering, and customer-obsessed problem solving to proactively identify mechanisms, process improvements, and products that simultaneously improve Amazon and Amazonians' lives, wellbeing, and value of work. As an interdisciplinary team combining talents from machine learning, statistics, economics, behavioral science, engineering, and product development, the Core Science team develops and delivers measurable solutions through innovation and rapid prototyping to accelerate informed, accurate, and reliable decision-making backed by science and data. We are building a talent intelligence layer — fusing natural language understanding, network science, and large-scale predictive modeling into a unified platform that continuously learns from how people work, collaborate, and grow across one of the world's largest and most complex workforces.

We are seeking a Product Manager, Data Strategy & Physical AI to define and execute the long-term product vision for FAR's AI-powered robotics platform. The intersection of foundation models and physical intelligence is creating a once-in-a-generation opportunity to reimagine how intelligent systems perceive, reason, and act in the real world. We need a visionary product leader who can treat data as our primary competitive moat and translate research frontiers into scalable, production-grade capabilities. In this role, you will champion our core data strategy for foundation model creation, building a partner and tool ecosystem to systematically acquire, label, and iteratively improve physical AI datasets. You will architect a continuous data collection flywheel across deployed robot fleets, transforming real-world kinematics, video, and force-torque telemetry from edge operations back into high-fidelity training tokens. Recognizing the limitations of real-world environments, you will also lead the strategy to create high-fidelity synthesized datasets, utilizing advanced physics engines and simulation to generate diverse training tokens at massive scale. Key job responsibilities Data Acquisition & Labeling Ecosystem: Establish the partnerships, tools, and vendor pipelines necessary to acquire, curate, and continuously label multi-modal datasets for training large-scale models. Fleet Data Flywheel Infrastructure: Architect the framework for a continuous data flywheel that securely streams high-frequency kinematics, egocentric video, and force-torque telemetry from real-world robot fleets back into the training loop. Synthetic Data & Simulation Strategy: Define the strategy for generating high-fidelity, physics-aligned synthesized datasets using advanced simulation environments to scale training tokens for edge-case scenarios and long-horizon tasks. Data Compliance & Governance: Partner with operations, privacy, legal, and security teams to build enterprise-grade data management pipelines that programmatically enforce data minimization, anonymization, and CCPA/GDPR compliance. Data Quality & Token Curation: Implement automated telemetry filtering and dataset pruning strategies to identify high-value operational logs, eliminate redundant fleet data, and optimize training compute costs. Cross-Functional Physical AI Delivery: Act as the strategic bridge between machine learning research scientists, simulation developers, robotics engineers, and hardware teams to deliver data-ready platform features that improve physical reliability. About the team At Frontier AI & Robotics, we're not just advancing robotics - we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence - from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Alexa International is looking for passionate, talented, and inventive Senior Applied Scientists to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. Senior applied scientists will drive cross-team scientific strategy, influence partner teams, and deliver solutions that have broad impact across Alexa's international products and services. Key job responsibilities As a Applied Scientist II with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs, particularly delivering industry-leading scientific research and applied AI for multi-lingual applications — a challenging area for the industry globally. Your work will directly impact our global customers in the form of products and services that support Alexa+. You will leverage Amazon's heterogeneous data sources and large-scale computing resources to accelerate advances in text, speech, and vision domains. The ideal candidate possesses a solid understanding of machine learning, speech and/or natural language processing, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environment, like to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and are able to influence and align multiple teams around a shared scientific vision. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using advanced and innovative techniques like SFT, DPO, Reinforcement Learning (RLHF and RLAIF) for supporting model performance specific to a customer’s location and language. * Quickly experiment and set up experimentation framework for agile model and data analysis or A/B testing. * Contribute through industry-first research to drive innovation forward. * Drive cross-team scientific strategy and influence partner teams on LLM evaluation frameworks, post-training methodologies, and best practices for international speech and language systems. * Lead end-to-end delivery of scientifically complex solutions from research to production, including reusable science components and services that resolve architecture deficiencies across teams. * Serve as a scientific thought leader, communicating solutions clearly to partners, stakeholders, and senior leadership. * Actively mentor junior scientists and contribute to the broader internal and external scientific community through publications and community engagement.

Application deadline: Jul 31, 2026 We are looking for an experienced Sr. Manager, Applied Science to own technical vision and roadmaps, drive innovation at AWS scale, and lead a team of applied scientists working at the frontier of automated reasoning and neurosymbolic AI. You will partner closely with security, privacy, and compliance stakeholders across AWS to expand the reach and impact of provably correct products. Key job responsibilities - Hire, develop, and retain a world-class team of applied scientists. - Foster a culture of scientific rigor, innovation, and operational excellence. - Own strategic design, implementation, and delivery of solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness of partner teams. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Science of Security is dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for security, privacy, and sovereignty.

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 ML 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 and reinforcement learning methods. 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 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 Bases to perform open information extraction at scale from visually rich documents. Key job responsibilities: - Using AI, NLP and advances in LLMs/SLMs and agentic systems to create scalable solutions for business problems. - Developing models for efficiently crawling web, automate extraction of relevant information from large amounts of Visually Rich Documents and optimize key processes. - Designing, developing, evaluating and deploying, innovative and highly scalable ML models, esp. leveraging latest advances in RL-based fine-tuning methods like DPO, GRPO etc. - Identifying latest technical/research trends applicable for the problems of efficient web navigation and web-scale information extraction and adapting them to concrete open problems. - Influencing software engineering teams to drive and optimize model implementations. - Challenging status quo in the current end-to-end production stack and ML models and identifying opportunities for simplification, improvements, cost-saving and innovation. - Establishing scalable, efficient, automated processes for large scale model development, model validation and model maintenance. - Leading projects and mentoring other scientists, interns, engineers in the use of ML techniques. - Publishing innovation in research forums.

Do you want to define the multi-year science vision that transforms how millions of customers experience AWS products? Do you want to influence the AWS investment in GenAI technology and see the impact of your leadership moving the needle on billions of dollars of AWS business? Do you want to lead cross functional team that impacts multiple organizations (product, sales, marketing, finance) in AWS? Do you want to push the boundaries of AI/ML technology (e.g. multi-agent analytics system, agentic knowledge representation and management, graph neural networks, reinforcement learning, causal inference, optimization, and LLM-based forecasting models) to build scalable ML products that help AWS grow and delight our customers? The AWS Analytics Engineering (AAE) is at the forefront of leveraging cutting-edge AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their data. We are a cross functional org from decision science, ML products, data platform, and agentic analytics system. Our vision is to use artificial intelligence and machine learning to enable AWS product teams, product, and go to market leaders to drive product growth and create personalized, optimized, and simplified product experiences to delight our customers. We shape AWS product features (e.g. Console, Spot and Autoscaling), influence GTM efficiencies with customer propensity models, democratize data and insights access through multi-agent system, and influence AWS leaders’ product strategy. We are looking for a customer-focused, solutions-oriented Senior Applied Science Manager to lead and define the science and engineering strategy across AWS product organization. In this role, you will set the technical direction for agentic analytics products, build ML features for AWS products to optimize their operations, influence product growth related decision science for senior leaders, generate ML-driven sales leads for AWS GTM teams, innovate multi-agent analytics system, develop big data engineering system at the AWS data scale. You will partner directly with GMs, VPs, and senior product leaders from major AWS product management, marketing, and sales organization to translate complex business challenges into innovative scientific solutions that directly influence AWS's top line and bottom line. As a Senior Applied Science Manager , you will be the technical thought leader who establishes the science roadmap, analytics software development, drives cross-organizational alignment, and raises the bar for scientific rigor across the team. You will work cross organization from AWS product management, engineering, sales, marketing, and finance. You will operate effectively in ambiguous environments, exercise strong business judgment on high-impact decisions, drive the innovation and publication roadmap, and continuously push the frontier of what's possible with ML-driven product intelligence at AWS scale. Key job responsibilities Define and drive the multi-year science, ML product, and software engineering vision and roadmap for ML-powered product analytics across AWS Products, Marketing, and Sales organization - Build, lead, and develop a high-performing team of technical managers, applied scientists, and software engineers, including hiring top talent, managing performance, and growing careers through mentorship and promotion readiness - Partner with senior AWS leaders (GM/VP level) to identify strategic, data-driven opportunities and translate business objectives into high-impact scientific initiatives - Architect and guide enterprise-scale ML and agentic platform, including agentic system, knowledge representation, big data platform, deep learning, graph neural networks, reinforcement learning, causal inference, and forecasting models that predict business outcomes and enhance customer experiences - Manage cross-functional science and software engineering team to build ML driven products that are scalable and leading industry best practices at the AWS scale and speed - Invent, operationalize, and scale novel analytical frameworks and metrics that enable data-driven product growth and executive decision-making - Communicate findings, conclusions, and strategic recommendations to technical and non-technical business leaders across AWS - Mentor scientists and engineers, establish best practices for experiment design and model evaluation, and review technical artifacts to ensure quality - Identify and champion new science opportunities that expand AAE’s impact across AWS, building the case for investment and driving adoption A day in the life As a senior applied science manager in AAE org, you will shape the science strategy that underpins product decisions across multiple AWS organizations. You'll spend your time partnering with VPs and GMs to identify the highest-leverage science opportunities, architecting novel ML solutions to complex product challenges, and mentoring scientists across the team. You'll drive alignment across cross-functional stakeholders, ensure scientific rigor in our most critical initiatives, and communicate insights that directly influence AWS product roadmaps. You'll balance long-term vision-setting with hands-on technical leadership, diving deep into model architectures and data pipelines when needed while maintaining the strategic altitude to guide the team's direction. You will manage cross functional science and engineering team to build cutting edge agentic system and ML products that transform our product and customer experience. About the team We are a team of scientists and software engineers supporting AWS product leaders to make high-impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We come from diverse backgrounds in statistics, computer science, engineering, and business analytics. We specialize in the full end-to-end ML development process, including data ingestion, ETL, model development, and model deployment in production. We provide AI/ML services across decision science, ML products, multi-agent analytics systems, and data engineering platform. High Impact Projects: We work on high-impact, high-visibility projects that directly influence AWS product roadmaps and senior leaders' decisions. Supportive Team Environment: We are proud of our supportive and inclusive team culture, we have each other's back during ups and downs. Work-Life Balance: We believe 80% of value comes from 20% of work, so we always prioritize our backlog ruthlessly based on business value. Learning Opportunity: Extensive opportunities to understand AWS business and leverage state-of-the-art AI/ML and cloud technology.

About us As part of the AWS Applied AI Solutions organization, our vision is to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. Our team combines Amazon's real-world experience with state-of-art AI to create opinionated, turnkey solutions that are no-brainers to buy and easy to use. We're building applied AI solutions that businesses love and trust. Our ambition is to become the partner companies rely on to run their business every day—putting AI to work to deliver better customer experiences, operational excellence, and faster innovation. We're a fast-moving, scrappy team building a new agentic product from the ground up. If bias for action is your favorite leadership principle, you'll fit right in. The Role We're seeking a talented Senior Applied Scientist with expertise in large language models, agentic systems, and foundational models. You will be responsible for building the state-of-art multi-agent system, using a handful of methods including fine-tunning, reinforcement learning, etc. You'll accelerate our customer-facing features, contribute to our collaborative and innovative culture, and bring state-of-art applied research that raises the bar for the entire team. Key job responsibilities • Drive end-to-end GenAI projects with high complexity and ambiguity from conception to production • Build, optimize, and deploy ML models while collaborating with software engineers for productionization • Research innovative machine learning approaches and identify new opportunities for GenAI applications • Perform hands-on analysis and modeling of large datasets to develop actionable insights • Establish scalable, automated processes for data analysis, model development, and validation • Present results to senior leadership and collaborate with cross-functional teams About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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. Why 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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. 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.

We are looking for a Senior Applied Scientist who will own the science strategy and technical direction for computer vision and machine learning within the Amazon grocery ecosystem. You will identify the highest-impact problems in ambiguous, rapidly evolving domains, frame them rigorously, and drive solutions from research through production at scale. You will lead cross-functional technical decisions with engineering, product, and business partners, shaping not just what we build but also how we invest. This is a role where your scientific judgment, architectural choices, and ability to create clarity from ambiguity will directly define the intelligence layer powering millions of grocery shopping experiences. Key job responsibilities * Own the end-to-end science strategy for computer vision and machine learning solutions in the grocery domain, navigating ambiguity to identify the highest-impact opportunities * Develop novel approaches to complex, unsolved perception and identification challenges where off-the-shelf methods are insufficient; publish findings internally or externally to advance the state of the art * Define the evaluation framework and success criteria for model performance, establishing metrics that connect scientific outcomes to measurable business impact and using these to influence roadmap prioritization * Lead cross-functional technical design with engineering, product, and operations partners, driving architecture decisions for model serving, data pipelines, and system reliability at scale rather than solely handing off models for productionization * Identify and resolve ambiguous, cross-team technical dependencies (e.g., upstream data quality, annotation infrastructure, model interoperability) that block progress across multiple workstreams; propose and drive solutions proactively * Influence technical direction beyond the immediate team mentor scientists, raise the bar in hiring, establish best practices for experimentation and model development, and represent the team's science strategy to senior leadership * Communicate complex technical trade-offs and recommendations to VP-level stakeholders, shaping investment decisions and aligning cross-org partners on science-informed product direction A day in the life As a Senior Applied Scientist on the GRAISE team, you'll own the technical strategy for how computer vision and multimodal learning come together to solve perception problems in grocery stores — many of which no one has cleanly formulated yet. On any given day, you might diagnose a surprising failure mode from overnight experiments and decide whether to pivot your approach entirely, co-architect a serving system with engineers while defining confidence thresholds and graceful degradation paths, present a precision-recall trade-off to senior leaders in terms that shape launch decisions and investment priorities, or unblock a cross-team dependency on annotation infrastructure — all while mentoring junior scientists and carving out time for deep technical work on problems the team hasn't cracked. Your scientific judgment and architectural choices will directly shape the shopping experience for millions of customers across Amazon's grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

Amazon Prime is building the future of AI-based personalization in driving long-term value out of the Prime subscription. Join our team of Scientists and Engineers developing AI/ML models to predict the Prime customer long-term behavior and optimize the customer experience with the Prime program. This includes identifying who our customers are, modeling customer behavior, and creating personalization systems to optimize the experience. As an AI/ML expert, you will partner directly with product owners to define the vision for this space and drive the roadmap for team execution. This is a complex personalization and optimization challenge. We sit at the intersection of massive-scale subscription and behavioral data, real-time decision systems, hundreds of millions of customers, and intersection with other shopping and benefit data across Amazon. Our vision is to enable a data-driven intelligent foundation that powers the future of Prime through scalable personalization, generation, and decisioning capabilities that serve optimal, cohesive customer experiences across all touchpoints. We build the science and infrastructure that allows Prime to know each member deeply, decide what to show them and when, generate the right content in the right format, and measure whether it worked — all optimized toward long-term member value. As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, cross-cutting organization, and drive breakthroughs that reshape how Prime thinks about customers and the membership program at the deepest level. You will pioneer next-generation Gen AI / LLM-based foundation models that build rich, evolving models of customer intent. This will include building Gen AI/transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. It will require thinking about real-time, multi-objective, globally scalable ranking systems that address cold start problems at billions of decisions per day. You will do all of this as a force multiplier, building foundational technology that empowers scientists and engineers, and driving teams across Prime and Amazon to deliver the best science outcomes for customers. Come define what the Prime membership program and experience looks like in the age of AI!

Amazon Ads is re-imagining advertising through generative artificial intelligence (AI) technologies. We combine human creativity with AI to transform every aspect of the advertising life cycle, from ad creation and optimization to performance analysis and customer insights. Our solutions help advertisers grow their brands while enabling millions of customers to discover and purchase products through delightful experiences. We deliver billions of ad impressions and millions of clicks daily, breaking fresh ground in product and technical innovations. 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. Why you’ll love this role: Amazon is investing heavily in building a world-class advertising business. To meet Advertiser goals, we process over 4 million ad request per second. This team defines and delivers optimization solutions for this enormous volume of traffic filtering unnecessary ad request and processing only whats in-demand and brings value for our Advertisers. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You'll work alongside talented engineers, and product leaders in a culture that encourages bias for action, innovation and experimentation, and you will directly influence business strategy through your scientific expertise. What makes this role unique is the combination of scientific rigor with real-world impact. You will re-imagine advertising through the lens of advanced ML while solving problems that balance the needs of advertisers, customers, and Amazon's business objectives. Your impact and career growth: Amazon Ads is investing heavily in AI and ML capabilities, creating opportunities for scientists to innovate and make their marks. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Learn more about Amazon Ads: https://advertising.amazon.com/ Key job responsibilities As a Senior Applied Scientist in Amazon Ads, you will: - Conduct hands-on data analysis, build large-scale machine-learning models and pipelines - Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams including engineers, product managers, and other scientists - Design and run A/B experiments to validate hypotheses, gather insights from large-scale data analysis, and measure business impact. - Research and implement ML approaches, including applications of generative AI and large language models. - Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, often working with petabytes of data. - Provide technical leadership, research new machine learning approaches to drive continued scientific innovation - Build supply optimization models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience