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.

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.

Applied Scientist II at Amazon Fulfillment Technology (AFT) Science, focusing on developing production solutions for Amazon's Fulfillment Network using operations research, optimization, statistics, machine learning, and GenAI/LLM. The role involves designing, building, and deploying scalable mathematical models and optimization-driven solutions to improve process efficiency and associate experience within the fulfillment network. Collaboration with scientists, software engineers, and product managers is key, with a focus on translating research into production systems.

Senior Manager role focused on leading AI/ML innovation for network planning solutions, involving demand forecasting and network optimization algorithms. The role requires developing and deploying production-ready models, architecting scalable AI/ML infrastructure, and driving scientific innovation from research to production, ultimately translating AI/ML capabilities into measurable business outcomes. It emphasizes leading a team of applied scientists and collaborating with cross-functional partners.

The Sr. Applied Scientist will focus on developing and implementing machine learning models and algorithms to optimize Amazon's Last Mile logistics operations. This role involves analyzing business challenges, creating metrics, developing scalable processes and tools, and working with technology teams to improve delivery efficiency and cost-effectiveness. The position requires a PhD in a quantitative field and significant experience in building ML models for business applications.

Senior Applied Scientist role focused on using ML/AI to understand demand-side signals in e-commerce, empower sellers to grow their business, and improve customer experience. Responsibilities include developing scalable seller-facing solutions, analyzing business data, designing and evaluating innovative models, and collaborating with engineering teams for real-time implementation.

Applied Scientist role at Amazon Ads focusing on Generative AI, AI Agents, and large-scale ML for demand forecasting. Responsibilities include leading ML initiatives, developing and optimizing forecasting models, building and deploying ML models, designing A/B experiments, establishing scalable ML infrastructure, and researching innovative ML techniques. Requires 3+ years of model building experience, a PhD or Master's with 4+ years of experience in a related field, and programming experience in Java, C++, or Python. Preferred qualifications include experience with experimental design and deep learning frameworks.

The role focuses on applying advanced analytical, statistical, and machine learning techniques to improve Amazon's Selling Partner experience through a Generative AI-powered conversational assistant. Responsibilities include analyzing seller pain points, evaluating feature performance, designing measurement frameworks (A/B testing, causal inference), applying NLP and statistical modeling to unstructured data, and partnering with cross-functional teams to influence product roadmaps. The goal is to enhance the Selling Assistant by responding to seller feedback and implementing fixes in the GenAI solution.

The Sr. Applied Scientist Pricing Optimization role at Amazon Leo focuses on building foundational ML models to optimize pricing and product feature strategies for a satellite broadband network. This involves predicting customer behavior, modeling multi-product bundling, and implementing scalable inference systems.

Software Development Engineer 2 role on the Unified Risk Evaluation System team at Amazon Business, focusing on fraud prevention and risk management using machine learning and scalable systems to process billions of transactions globally. The team builds self-service capabilities for ML model experimentation and manages numerous risk use cases.

Senior Software Engineer to lead the design and evolution of AI-driven systems for generating, selecting, and optimizing marketing and merchandising assets at scale across Amazon worldwide. The role involves building systems that automatically produce and adapt images, text, and multimodal content, balancing creative flexibility, brand constraints, and performance. It requires end-to-end ownership across model integration, distributed services, and measurement frameworks, with a focus on architectural leadership and mentoring.

Lead ML teams building large-scale forecasting and optimization systems for Amazon's global transportation network, impacting customer experience and cost. The role involves setting scientific direction, mentoring scientists, and delivering production-grade ML solutions at scale, using techniques like deep learning and reinforcement learning.

Senior Applied Scientist role focused on developing next-generation advanced robotics systems that combine AI, control systems, and mechanical design for automation at Amazon's scale. The role involves research and implementation of deep learning and LLMs for robotics, with a focus on simulation, physics-based modeling, and sim-to-real gap reduction.

Applied Scientist role focused on developing and implementing forecasting and optimization models using ML and GenAI to support real estate and store planning decisions for Amazon's grocery business. The role involves analyzing large datasets, building end-to-end solutions, and collaborating with cross-functional teams to drive adoption of insights and models.

Data Scientist for Prime Air focusing on applied ML and experimentation to improve customer journey, drive expansion, and optimize delivery operations. The role involves developing statistical and ML techniques, building predictive models, designing experiments, and presenting findings to leadership, with a focus on shipping models that have a direct business impact.

Senior Manager, Applied Science for Marketing Measurement and Performance Science (MAPS) at Amazon. This role focuses on building scalable ML and causal inference solutions to estimate marketing effectiveness and provide insights for marketing teams. It involves leading a team of scientists, developing end-to-end causal inference models, and influencing multi-billion dollar investment decisions. The role requires expertise in ML/DL, statistics, economics, and handling large datasets at scale, with a focus on delivering data-driven solutions that impact business strategy and customer behavior.

Manager for an Applied Science team focused on developing and deploying machine learning and optimization solutions for Amazon's publisher monetization platform. The role involves leading science and engineering teams, defining technical roadmaps, and collaborating with stakeholders to drive business impact at scale.

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.

Software Development Manager for AWS Just Walk Out technology, focusing on building and enhancing the checkout-free shopping experience. The role involves managing a team of 6-10 engineers to deliver critical capabilities, leveraging vision, sensor fusion, and machine learning. The team builds the platform and architecture for retail capabilities, including order management and merchant integration.

Applied Scientist role focused on designing and building machine learning solutions for customer discovery of Amazon's Private Brands. The role involves end-to-end project management from ideation to launch, with a strong emphasis on causal ML, deep learning, and deploying models to production. The goal is to drive customer awareness and product discovery, impacting Amazon's own brands and contributing to broader discovery solutions across the company.

Applied Scientist II role at Amazon Ads focusing on Sponsored Products and Brands, specifically the Search Ranking and Interleaving (R&I) team. The role involves developing and applying GenAI and ML techniques to optimize ad ranking, allocation, and relevance on the search page. Key responsibilities include building real-time ML algorithms for ad auctions, developing multi-objective optimization methods, and researching new ML approaches to improve shopper experience and drive revenue. The role requires experience in building models for business applications and applying theoretical models in practice.

Manager for Navigation Science team at Amazon Prime Air, focusing on developing advanced navigation algorithms and flight software for delivery drones. Requires leadership experience, strong programming skills, and experience with agile development. The role involves defining system architectures, contributing to flight algorithms, and driving innovation in a safety-critical environment.

Software Development Engineer II role focused on building AI-native experiences for Alexa+ using generative AI and LLMs, empowering third-party partners to create engaging customer experiences. The role involves developing software for millions of devices, focusing on architecture, quality, and engineering best practices.

Sr. Manager of Applied Science with expertise in Statistical Machine Learning and/or Reinforcement Learning to drive research, development, and deployment of AI technology for supply chain optimization. The role involves managing a team of scientists, focusing on next-generation solutions for buying, placement, and fulfillment decisions, and translating customer needs into reusable software infrastructure. Key responsibilities include leading innovation in RL applications, building and developing a high-performing team, bridging technical possibilities with business requirements, and driving cross-functional collaboration.

Applied Scientist role focused on building machine learning models and technology to automatically monitor and classify billions of products in the Amazon catalog, ensuring compliance and safety. The role involves working with large datasets, deep learning for search matching and ranking, filtering, indexing, and document understanding, with a focus on delivering customer-facing experiences at Amazon scale.

Master's student internship focused on applying machine learning, NLP, computer vision, automated reasoning, or robotics to design and develop end-to-end systems for Amazon Science. The role involves working with large datasets, implementing systems, and potentially deploying solutions into production.

Senior Applied Scientist role focused on developing and launching end-to-end AI solutions, specifically recommendation systems leveraging generative models, for Amazon's Sponsored Products and Brands advertising platform. The role involves defining science vision, designing experiments, and collaborating with engineering and product teams to improve advertiser and shopper experiences.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods. Focus on designing and implementing state-of-the-art solutions for complex problems, potentially leading to production deployment and publication.

The Sr. Applied Scientist role in Amazon Logistics focuses on optimizing last-mile delivery operations through the development and application of machine learning models and algorithms. The role involves estimating costs, improving metrics, developing scalable processes, and working with technology teams to build new tools and systems that impact customer experience and delivery efficiency. It requires a PhD or equivalent experience in a quantitative field and strong analytical and program management skills.

MS student internship focused on building and deploying machine learning models for Amazon's business problems, with potential for worldwide scale. The role involves working with large datasets and cloud technology, and defining metrics for customer satisfaction.

Data Scientist III on the Shopbop/Zappos Catalog Tech team responsible for designing and implementing ML approaches to improve product catalog data quality, automate data capture and classification, and integrate ML models into production systems. The role involves working with computer vision and NLP, and influencing product decisions through data-driven insights.

Senior Machine Learning Engineer role focused on developing and launching AI solutions for Prime Video's recommendation and personalization systems, utilizing deep learning, GenAI, and reinforcement learning. The role involves end-to-end ownership, experimental design, and collaboration with cross-functional teams to impact millions of customers.

Manage a team of Applied Scientists, ML Engineers, and Software Development Engineers to develop science and engineering roadmaps for SPB ads CTR prediction using ML and Gen AI solutions. The role involves hiring, developing talent, and staying informed about scientific publications and industrial research trends. The team focuses on personalized shopping experiences through ML and GenAI solutions for response prediction and session-level understanding to optimize ad serving, targeting, sourcing, and bidding.

This role leads science and engineering for AI-powered sponsored product ads in offsite shopping experiences, focusing on generative AI and large-scale ML solutions. The goal is to revolutionize advertising by bridging human creativity with AI, impacting ad creation, optimization, performance analysis, and customer insights. The role involves extending campaigns to reach customers off-store and on third-party sites, working with external and internal partners, and driving results at scale with a GenAI-first approach. It requires significant experience in building large-scale ML/AI solutions and people management.

Principal Applied Scientist role focused on building next-generation personalized shopping experiences at Amazon using LLMs, transformer models, and large-scale ranking systems. The role involves innovating features and models, leading science innovation, driving the science roadmap, and mentoring scientists. It aims to create a personalized shopping experience tailored to customer intent and product catalog understanding.

Applied Scientist role focused on building and deploying machine learning models for Amazon's consumer businesses. Responsibilities include analyzing large datasets, designing and evaluating scalable models, and collaborating with engineering teams for real-time implementation. The role emphasizes end-to-end ownership of business problems and optimizing operations through ML.

Senior Machine Learning Engineer role at Amazon Prime Video focused on developing and launching AI solutions for recommendation and personalization systems. The role involves end-to-end ownership of ML models, including design, implementation, experimentation (A/B testing), and deployment for millions of customers. It requires experience with large-scale ML systems and recommendation systems.

This role focuses on developing and launching end-to-end AI solutions for Prime Video's search and discovery systems, utilizing deep learning, GenAI, and reinforcement learning. The scientist will design and conduct experiments, collaborate with engineers and product managers, and publish research findings. The role is within the consumer domain, aiming to improve customer experience for millions of users.

Senior Applied Scientist role at Amazon Advertising focused on building and deploying end-to-end machine learning models to improve traffic monetization and merchandise sales. The role involves leading ML efforts, performing hands-on analysis, driving ambiguous projects, and establishing scalable processes for model development and deployment.

Senior Applied Scientist role at Amazon Prime Video focusing on Playback Intelligence. The role involves applying machine learning and data science to optimize video streaming quality, detect anomalies, and leverage LLMs and generative AI. Responsibilities include end-to-end ownership of product and user experience, translating business requirements into ML deliverables, defining research directions, conducting experiments, and mentoring junior scientists. The role requires experience in building ML models for business applications and designing AI solutions for real-world use cases.

Principal Applied Scientist role focused on applying machine learning, optimization, and economics to improve Amazon's Stores business, specifically in areas like delivery speed, seller fees, and LLM applications. The role involves leading a team, developing scientific models, benchmarks, and services, and deploying solutions in partnership with product teams.