This role focuses on applying ML and AI to solve business problems within Amazon's third-party marketplace, specifically concerning seller fees. The scientist will develop and deploy models for fee accuracy, policy automation, and seller explanations, working closely with engineering and product teams to productionize solutions. The role emphasizes rigorous experimentation, causal inference, and contributing to the scientific culture.

Software Development Manager for Amazon Ads' Sponsored Products and Brands team, focusing on leveraging generative AI and ML to revolutionize advertising experiences. The role involves managing engineers, developing roadmaps for ML/GenAI solutions in ad retrieval, fostering cross-team collaboration, and hiring/developing talent. The team operates full-stack, from backend services to shopper-facing experiences, aiming to deliver personalized and context-aware advertising.

This role focuses on applying machine learning to video processing, understanding, and content monetization within AWS Elemental. The scientist will prototype, optimize, and deploy advanced models into production, collaborate with cross-functional teams, and develop scalable solutions. The role also involves mentoring, contributing to operational tooling, and potentially publishing applied research. The primary focus is on shipping AI-powered features and enhancements for video services.

This role focuses on architecting and developing end-to-end solutions that leverage Generative AI technologies like Amazon Quick and Bedrock for customer success. It involves designing and implementing solution blueprints, integrating Gen AI capabilities into existing systems, and providing guidance on data preparation, model training, and deployment strategies. The role also includes customer updates and SFDC hygiene.

Software Development Engineer to build and improve a generative AI-powered shopping assistant (Rufus) for millions of Amazon customers, focusing on product research, comparisons, and recommendations.

PhD student internship focused on applying machine learning, NLP, computer vision, automated reasoning, or robotics to solve complex business problems and deploy solutions into production at Amazon.

Machine Learning Engineer role focused on designing, implementing, and scaling Generative AI solutions for enterprise customers on AWS. The role involves working directly with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and provide technical guidance throughout the project lifecycle. It emphasizes customer success and driving AI transformation.

This role focuses on building and deploying machine learning models to improve customer experience through personalized recommendations on Amazon's retail website. The scientist will leverage large-scale data and computing resources to understand customer behavior, develop state-of-the-art recommendation models, and measure their impact on the business.

Senior Applied Scientist role focused on developing and improving sensing systems for advanced robotics at Amazon scale. This involves designing custom sensor systems, integrating AI and control systems, and working from concept to production in a fast-paced, interdisciplinary environment. The role requires deep knowledge in sensing, signal processing, computer vision, and machine learning, with a focus on shipping robotic products.

Senior Applied Scientist role focused on developing and deploying large-scale machine learning systems for fraud and risk prevention in an e-commerce environment. The role involves leading scientific strategy, designing and deploying ML systems, and leveraging Generative AI and LLMs to build next-generation platforms. It requires experience in building ML models, deep learning, and programming languages.

This role focuses on building and deploying advanced machine learning models for Amazon's consumer businesses in India and other emerging markets. The scientist will analyze large datasets, develop and evaluate scalable ML solutions, and work closely with engineering teams to implement these models in production. The role involves end-to-end ownership of business problems and directly impacting profitability.

Senior Applied Scientist role at Amazon Prime Video focused on developing and launching AI solutions for personalization and discovery, including search systems. The role involves end-to-end ownership of machine learning models, from design and implementation to experimentation and evaluation, with a focus on benefiting millions of customers. The position also requires staying updated with the latest modeling techniques and publishing research findings.

Senior Applied Scientist role focused on developing and productionizing AI/ML models for ad measurement and performance analysis within Amazon Ads. The role involves end-to-end solution development, leveraging Generative AI, classical ML, NLP, and Computer Vision to provide insights and automate workflows for advertisers. Responsibilities include model development, experimentation, collaboration with engineering and business teams, and mentoring junior scientists.

Applied Scientist role at Amazon Ads focusing on measurement science, developing solutions for performance measurement products, sales impact analysis, and AI tools for customer insights and workflow automation. The role involves end-to-end ownership of measurement solutions, from design to production, leveraging Generative AI, classical ML, Causal Inference, NLP, and Computer Vision.

The Sponsored Products and Brands team at Amazon Ads is seeking an Applied Scientist to leverage generative AI and machine learning to revolutionize advertising experiences. This role involves driving end-to-end ML projects, analyzing large datasets, building and deploying models into production, and establishing scalable processes for ML development and serving. The focus is on creating innovative ad tech and shopping experiences off the store, working with external and internal partners to connect systems, understand customers, and drive results at scale, with a GenAI-first approach.

Senior Applied Scientist role focused on developing and launching AI solutions for Amazon's Sponsored Products and Brands advertising business. This involves building recommendation systems leveraging generative models, designing and implementing end-to-end AI solutions, and defining a long-term science vision. The role requires experience in building ML models, deep learning, and working with large-scale distributed systems.

Manager for an AWS Applied AI Solutions team focused on building foundational systems and consumer-facing features for trustworthy AI experiences at scale within the supply chain domain. The role involves leading engineering teams, driving technical strategy for privacy-preserving and responsible AI, and developing enterprise-grade AI applications.

The Applied Scientist will build next-generation conversational shopping experiences for Amazon Rufus, developing data-driven solutions and deploying models into production. This role involves collaboration with cross-functional teams and leveraging Amazon's data and AWS resources.

This role focuses on designing, developing, and deploying full-stack applications and data-intensive systems for supply chain optimization and automation. It involves building scalable backend services, frontend applications, data pipelines, and implementing end-to-end machine learning solutions within the Amazon production environment to improve supply chain forecasting and automation.

Manager of Data Science at AWS responsible for overseeing a team that builds large-scale distributed systems for abuse detection. The role involves defining business goals, aggregating data, managing research vision, mentoring scientists, and ensuring successful integration of AI models into production. Requires experience in managing data science teams, cross-functional projects, ML techniques, generative AI, foundation models, MLOps, and production deployment of AI systems.

Manage a team of engineers building ML and generative AI systems for retail operations automation, focusing on catalog management, supply chain, and operational efficiency. The role involves architectural decisions, mentoring, and cross-team coordination to deliver scalable, reusable platforms.

Applied Scientist role focused on building machine learning models for Amazon Search, developing new ranking features, and improving the ML framework for training and offline evaluation. The role involves designing, developing, and implementing production-level code for billions of search requests, with an emphasis on low-latency predictions and high throughput systems.

Software Development Engineer role focused on building and deploying computer vision and machine learning systems for Amazon's global fulfillment network. The role involves end-to-end ownership of systems that provide vision-based insights for item identification, defect detection, and decision-making in manual and robotic operations, supporting downstream business processes and flagship devices.

Applied Scientist II role at Amazon Ads focused on building production ML and bandit solutions to customize the Sponsored Products search experience. The role involves determining ad placement, quantity, and targeting using machine learning, causal inference, and optimization techniques. It emphasizes improving shopper discovery and advertiser reach by modeling shopper responses and optimizing ad allocation and ranking on the search page, with a recent focus on integrating LLMs/GenAI for production use cases.

Applied Scientist role focused on building machine learning and optimization models for Amazon's large-scale transportation planning systems, including dynamic pricing and network planning. The role involves designing, building, and implementing scalable products and algorithms within production systems, with a focus on external freight and marketplace operations.

Data Scientist role focused on building AI/ML-powered procurement applications using machine learning, NLP, and predictive analytics. The role involves designing, developing, and deploying models and data pipelines, translating business requirements into data-driven solutions, and impacting daily operations for Amazon's global workforce.

Software Development Engineer role at Amazon Alexa Connections, focusing on integrating generative AI and machine learning into communication features to enhance customer experiences. The role involves designing and building scalable, fault-tolerant systems for AI/ML workloads, including Gen AI inference and training pipelines, within an agile environment.

Manager for an Applied Scientist team focused on optimizing the returns and re-commerce process using ML, NLP, and Information Retrieval. The role involves managing scientists and engineers, developing ML models for business applications, and building scalable software systems.

Senior Applied Scientist role focused on developing AI-powered Discovery systems for AWS Marketplace, specializing in personalization, search, and recommendation systems. The role involves leading research, design, and development, translating scientific techniques into scalable solutions, and mentoring other scientists. It aims to accelerate cloud migrations and support partner growth.

This role focuses on delivering secure Generative AI solutions to enterprise customers, acting as both a security leader and hands-on practitioner. It involves conducting security assessments, architecting and implementing security controls for GenAI applications, and building reusable security patterns and accelerators. The role also involves developing innovative GenAI-powered security solutions and contributing to thought leadership.

The role involves designing and implementing machine learning models for recommendation systems to improve Amazon's talent acquisition processes. It focuses on building ML products that match job seekers with opportunities and recruiters with talent, operating at a global scale. The work involves representation learning, reinforcement learning, and probabilistic modeling, with an emphasis on fairness and explainability.

Applied Scientist role at Amazon Prime Video focused on building machine learning systems for content presentation and discovery. The role involves working with multi-modal embeddings, contextualized ranking, reinforcement learning, and recommender systems to improve customer engagement with the Prime Video catalog.

Amazon Search team is looking for an Applied Scientist II to build machine learning models for Product Search, develop new ranking features, and implement production-level code for search systems. The role involves owning the full development cycle from design to deployment and A/B testing, with a focus on customer experience and scalability.

This role focuses on building and improving machine learning models for Amazon's vertical search, specifically developing new ranking features and techniques. The scientist will be responsible for the full development cycle, from design and implementation to A/B testing and production deployment, aiming to enhance search quality and effectiveness across Amazon's product ecosystem.

Applied Scientist role focused on building and deploying end-to-end machine learning solutions to prevent eCommerce fraud and manage transaction safety within Amazon's production environment. The role involves innovating with GenAI/LLM/VLM technologies, analyzing large datasets, and creating automated risk evaluation and operational systems.

This role focuses on applying machine learning and AI to predict and reconcile product measurements for Amazon's third-party marketplace, impacting fee strategy and seller experience. The scientist will design, develop, and deploy AI/ML models, working closely with engineering and product teams to productionize solutions and validate their business impact through rigorous experimentation.

Applied Scientist role at Amazon Business Ads focusing on building and deploying scientifically novel software solutions for B2B advertising products. Requires experience in model building for business applications and proficiency in Python/deep learning frameworks.

Applied Scientist role focused on designing, developing, and deploying ML and computer vision models for Amazon's in-store grocery technologies, including smart carts, inventory intelligence, personalization, and store operations. The role involves end-to-end model development from ideation to production, with a focus on solving real-world problems at scale within the grocery domain.

Develops and deploys data-driven models for shopping personalization and recommendation systems, leveraging GenAI techniques and causal machine learning. Focuses on improving customer experience through supervised and uplift learning, A/B testing, and harnessing large-scale data. The role involves publishing research and mentoring junior scientists.

This role is for a Senior Applied Science Manager on the Stores Economics and Science team at Amazon. The team uses economics, statistics, and machine learning to understand and design Amazon's buyer and seller economy. The manager will provide structure for complex business problems, work with ML scientists to validate models, and help partners translate analysis into impactful actions. The role requires experience in building large-scale ML/AI solutions, leading scientists, and developing junior talent.

Machine Learning Engineer focused on developing and deploying ML products for network management within Amazon Infrastructure Engineering, aiming to improve customer experience and revolutionize business operations.

Senior Applied Scientist role focused on leveraging generative AI and machine learning to re-imagine advertising experiences for Amazon Ads' Sponsored Products and Brands. The role involves leading ML efforts, analyzing large datasets, building and deploying ML models for insights, traffic monetization, and personalized shopping experiences, and driving end-to-end ML projects. It also includes running A/B experiments, establishing scalable processes, researching new ML approaches, and recruiting/mentoring other scientists.

Applied Scientist II role at Amazon Ads focused on building production ML and bandit solutions to customize the Sponsored Products search experience. The role involves determining ad placement, quantity, and targeting using machine learning, causal inference, and optimization techniques. It emphasizes improving shopper discovery and advertiser reach by modeling shopper responses and optimizing ad allocation and ranking on the search page, with a recent focus on integrating LLMs/GenAI for production use cases.

Applied Scientist role focused on developing and launching AI solutions for Prime Video's search and personalization systems, involving deep learning, GenAI, and reinforcement learning. The role requires end-to-end ownership from design to launch, including experimentation and evaluation, with a strong emphasis on delivering customer-facing products. Research publication is also a component.

The Applied Scientist will lead Machine Learning efforts for Amazon Ads' Demand Forecasting & Guidance team. This role involves hands-on analysis and modeling of large datasets to develop insights, build and deploy ML models into production, and run A/B experiments. The goal is to create best-in-class forecasting products for advertisers to predict campaign outcomes and optimize ad performance, directly impacting key business decisions.

Applied Scientist II at Amazon focused on improving shopping experiences using LLMs. Requires 3+ years of building models for business applications, a PhD or Master's with 4+ years of experience, and a publication/patent record. Experience with algorithms, data structures, optimization, data mining, and parallel computing is expected.

Applied Scientist role focused on building and improving machine learning models for buyer risk prevention at Amazon. This involves analyzing large datasets, developing and deploying models, and collaborating with engineers and business teams to safeguard transactions and enhance risk policies. The role also includes experimenting with Generative AI and LLMs for automation and risk evaluation.

Applied Scientist role focused on building and delivering state-of-the-art science and engineering solutions for Amazon's Stores business, leveraging machine learning, optimization, and economics to improve business metrics. The role involves developing and maintaining scientific models, benchmarks, and services, and deploying solutions in partnership with product teams.