Research scientist role focused on developing and launching new AI technologies for personalization in e-commerce, leveraging LLMs, recommendation systems, and information retrieval.

Principal Product Manager, Technical for Alexa Connections, focusing on building AI-native communication experiences using generative AI, LLMs, real-time communications, voice, and GUIs. The role involves strategizing, driving product roadmaps, and launching 0-1 product lines, working cross-functionally with engineering, science, and design teams to deliver delightful customer experiences.

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.

Principal Product Manager Tech for Prime Video Personalization & Discovery, focusing on owning the vision, strategy, and roadmap for core recommendation systems and ML models. The role involves translating business problems into model requirements, defining metrics, leading A/B testing, and making trade-off decisions for ML-powered features at scale, with a strong emphasis on customer value and business outcomes.

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.

Senior Product Manager, Technical for Trustworthy Shopping Experience at Amazon. This role focuses on defining product strategy and roadmaps for Generative AI applications to automate compliance workflows, enhance decision-making, and scale safety operations. It involves partnering with Applied Scientists and Engineers to evaluate LLMs, design human-in-the-loop systems, establish AI governance, and ensure responsible AI deployment in compliance scenarios. The role requires technical depth in Generative AI and LLM-based systems, with a focus on production deployment and scaling.

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.

Applied Scientist role focused on developing and deploying personalized recommendation systems using state-of-the-art modeling and GenAI techniques for Amazon's consumer shopping experience. The role involves building data-driven models, developing new signals, using supervised and uplift learning, contributing to production code and tooling, designing A/B tests, and publishing research.

This role focuses on developing and deploying personalized recommendation systems for Amazon customers, integrating GenAI techniques, and conducting research. The primary deliverable is a customer-facing AI product.

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 focuses on developing and deploying data-driven models for personalized shopping experiences using state-of-the-art recommendation systems and GenAI techniques. The scientist will design, develop, evaluate, and update models, create new signals, use supervised and uplift learning, design A/B tests, and work with large-scale distributed machine learning. The role also involves presenting and publishing research, mentoring junior scientists, and collaborating with business and engineering teams. Experience with causal machine learning and evaluating AI systems is required.

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 developing and launching AI solutions for Prime Video's personalization and discovery systems, involving deep learning, GenAI, RL, and optimization. The role requires end-to-end ownership, experimental design (offline and A/B testing), and publishing research findings. Experience with recommender systems and personalization is preferred.

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.

Software Development Engineer role focused on building and managing machine learning models and platforms for fraud prevention and risk management at Amazon scale. The role involves developing self-service capabilities for experimenting with new ML models and managing numerous risk use cases globally, operating at the intersection of Machine Learning and Security.

Principal Product Manager for Prime Video's Personalization and Monetization, focusing on ML-powered features, recommendation systems, and customer experience. This role involves defining product strategy, translating business problems into algorithmic requirements, and guiding the ML lifecycle to ship scalable, customer-centric AI features.

Applied Scientist II role focused on building and deploying end-to-end machine learning models for fraud and risk prevention in an e-commerce environment. The role involves leveraging Generative AI and LLMs to enhance detection systems, analyzing large datasets, and collaborating with engineering and business stakeholders. Requires experience in model development from problem formulation to production, with a focus on scalable ML solutions.

Applied Scientist role focused on developing and launching customer-obsessed pricing algorithms for billions of products worldwide. This involves leveraging large-scale multi-modal datasets, applying machine learning, predictive modeling, causal inference, and reinforcement learning to optimize pricing strategies and tools for sellers. The role emphasizes collaboration with product and engineering teams, continuous learning, and delivering business impact through innovative pricing solutions.

Applied Scientist role focused on developing and productizing AI solutions for enterprise services within AWS. The role involves designing and implementing ML systems for diverse applications, creating scalable algorithms, conducting experimentation with advanced techniques (LLMs, CV, agentic AI), and collaborating with engineering and product teams to deliver customer impact. The primary output is shipped AI products, with a secondary focus on agentic AI systems.

Software Development Engineer role focused on building and scaling Amazon's next-generation recommendation system (RecsAI) which uses LLMs trained for shopping to provide personalized product suggestions. The role involves designing, building, testing, and operating features for this system, collaborating with product and science teams, and ensuring maintainability, scalability, performance, and reliability at Amazon scale. It emphasizes end-to-end ownership, experimentation, and integrating generative AI into real-time recommendation experiences.

Applied Scientist role focused on building and refining Generative AI and ML models to optimize Amazon's Ad Sales business. The role involves conceptualizing research, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. The goal is to transform account team operations with actionable insights, recommendations, and GenAI integration for improved efficiency.

The Applied Scientist II role on the Amazon Search team focuses on inventing signals and algorithms for machine-learned ranking models to improve search quality for millions of customers. The role spans the entire development pipeline, from data analysis and prototyping to A/B testing and production deployment.

Software Development Engineer II role within AWS Applied AI Solutions, focusing on the Just Walk Out (JWO) checkout-free retail technology. The role involves designing, developing, and deploying reliable distributed services that leverage computer vision and machine learning to create a seamless shopping experience. The engineer will contribute to inventing new features, prototyping, and mentoring other engineers, working with languages like Java and Python on the AWS cloud stack.

Data Scientist II at Audible (Amazon) focused on building production-ready LLM solutions and recommendation systems. Requires a Master's degree and 2+ years of experience in data science, ML modeling, Python/R, and non-linear models. Experience with LLMs (programmatic access, evaluation, fine-tuning), recommendation systems, and SQL/ETL is required.

Software Development Engineer II on the Featured Merchant Algorithm team at Amazon. This role involves working with machine learning models to select the best offer for customers on Amazon's product detail pages and search results. The team focuses on optimizing customer experience by considering factors like price, availability, and delivery options. The engineer will collaborate with various stakeholders to strategize and launch new features, using data to drive decisions and present impact.