Software Development Engineer II role on the Same Day Delivery Experience team, focusing on building and scaling AI-powered tools using LLMs, RAG, NLP, prompt engineering, and agentic AI workflows to enhance customer experience and protection. Responsibilities include designing and building AI tools, developing retrieval pipelines, prototyping agentic AI capabilities, and working on scalable AI/ML systems.

This role focuses on designing and developing machine learning solutions for customer behavior analytics at Amazon. Key responsibilities include fine-tuning language and generative models, developing recommendation and decision models, building temporal representations of customer behavior, and applying post-training optimization techniques. The role also involves developing evaluation frameworks and working with business and engineering teams to drive personalized customer experiences and business impact.

This role focuses on developing and scaling a machine learning compiler for AWS Neuron, which optimizes the performance of neural network models on custom AWS hardware accelerators (Inferentia and Trainium). The engineer will architect and implement features for the compiler stack, which integrates with popular ML frameworks, aiming to improve inference and training performance for large ML workloads.

This role focuses on developing and applying ML models, including foundation models, for recommendation and search systems within Prime Video's personalization and discovery science team. The goal is to enhance customer experience by recommending titles effectively and enabling discovery of niche interests. The role involves end-to-end ownership, experimentation, and collaboration with scientists, engineers, and product managers, with an emphasis on publishing research findings.

Senior Applied Scientist role focused on developing and launching foundation models for content understanding and customer behavior prediction within Prime Video. The role involves hands-on machine learning, research leadership, and end-to-end ownership of solutions, with an emphasis on publishing research findings.

Applied Scientist II at Amazon Prime Video focusing on personalization and discovery. The role involves developing foundation models for content understanding (video, text) and customer behavior prediction using deep learning and multimodal techniques. Responsibilities include building time sequence models, end-to-end solution implementation with engineers and product managers, designing and conducting A/B experiments, and publishing research findings. The team works on recommendation science for Prime Video surfaces and devices, aiming to solve cold-start problems and discover niche customer interests.

Sr. Applied Scientist at Amazon Prime Video focused on developing and launching AI solutions for personalization and discovery systems, impacting millions of customers.

This role focuses on developing and applying ML models, including foundation models, for recommendation and search systems within Prime Video's personalization and discovery science team. The goal is to enhance customer experience by recommending titles effectively and enabling discovery of niche interests. The role involves end-to-end ownership, experimentation, and collaboration with scientists, engineers, and product managers, with an emphasis on publishing research findings.

This role focuses on managing AI-focused customer engagements end-to-end, partnering with engineers and designers to deliver AI solutions using technologies like LLMs, RAG, and autonomous agents. The role involves orchestrating customer engagements, facilitating solution design, identifying opportunities, building relationships, and ensuring responsible AI practices.

Sr. Applied AI Solutions Architect focused on accelerating customer adoption of Amazon Connect's AI capabilities. The role involves guiding customers in model selection (via Amazon Bedrock), prompt configuration for AI agents, and architecting tool integrations (APIs, Lambda, etc.) for agentic AI systems. A key aspect is ensuring customer data readiness for AI agents and RAG. The role is hands-on, requiring coding, building integrations, and configuring agents, working at the intersection of contact center operations and applied AI.

Director of Science, Geospatial at Amazon, leading a team of ~50 scientists focused on AI/ML solutions for last-mile delivery operations. The role involves developing and deploying solutions for geospatial problems, including address validation, place datasets, road networks, and leveraging edge data. Key focus areas include GenAI (LLMs, VLMs, agents), computer vision, and traditional ML to optimize delivery routes, improve data fidelity, and drive business impact. The role requires interfacing with senior stakeholders, strategic planning, and building a high-performing team.

Senior Software Development Engineer to lead AI/ML/LLM/VLM/VLA development for automating manufacturing engineering workflows, including agentic AI, robotic control, and computer vision for quality assurance.

Research and develop generative AI technology for Amazon Connect, focusing on LLM Agents and their evaluation/optimization to disrupt customer service experiences. The role involves building ML models from conception to deployment, prototyping, and iterating on state-of-the-art Agentic AI systems.

This role manages a team of applied scientists and engineers focused on developing ML and RL algorithms for robotic systems to optimize stow strategy and warehouse capacity. It involves leading research, design, deployment, and evaluation of these systems, with a focus on transformer architectures, affordance learning, and geometric reasoning in high-density environments.

Senior Applied Scientist role focused on developing and deploying AI/ML models and analytics for customer-facing AI solutions within Amazon Connect. The role involves working directly with customers to accelerate production deployments, designing and building AI solutions, conducting experiments, quantifying business value, and applying NLP/generative AI techniques. It spans conversational analytics and agentic AI capabilities, with a strong emphasis on driving measurable business impact and operational excellence in customer environments.

This role focuses on applying Generative AI and LLMs to transform ad experiences on Amazon's homepage and other surfaces, impacting product discovery and customer engagement. It involves building and deploying models for ad retrieval, auctions, and personalized shopping experiences, operating across the full stack from backend systems to the user-facing layer.

The role focuses on developing and applying cutting-edge simulation methodologies for advanced robotics systems, including physics-based simulation, sim-to-real transfer, and machine learning. The goal is to enable rapid development, testing, and validation of robotic systems in complex environments. The role involves fundamental research and real-world development, translating research into scalable simulation capabilities that impact robot design and building.

Seeking an Applied Scientist with expertise in AI, Agentic LLMs, Generative AI, Machine Learning, and NLP to build LLM-powered solutions for Amazon's BuyForMe product. The role involves developing agentic frameworks, LLM fine-tuning, reinforcement learning, prompt engineering, RAG, MCP, and automated benchmarking to improve shopping workflows.

Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage.

This role leads the science strategy and technical vision for an intelligence layer using GenAI and predictive modeling, focusing on heterogeneous signals to power talent applications at Amazon scale. The Principal Applied Scientist will guide a team, conduct hands-on research in areas like foundation models and multi-modal LLMs, design novel ML architectures, and mentor scientists while contributing technically to complex problems.

Senior Applied AI Solutions Architect for Federal Financial Regulatory customers, focusing on designing and enabling AI/ML solutions for fraud detection, market surveillance, regulatory reporting, and consumer protection. The role involves technical guidance, developing reference architectures, and enabling customer adoption of AI/ML on AWS, with a strong emphasis on agentic systems and RAG.

Applied Scientist II role focused on developing advanced robotics systems using AI, deep learning, and reinforcement learning for automation at Amazon's scale. The role involves designing and implementing control methods for balance, locomotion, and manipulation, with a focus on bridging theoretical advancements and practical implementation in robotics.

Manager for the Amazon Sponsored Agent (ASA) team, focusing on building and scaling a new agentic service for conversational and agentic ads. The role involves leading a team to develop a multi-agent system architecture for contextual ad serving, conversation understanding, and commercial insights generation, with a focus on AI-native ad formats.

This role leads the science function in WW Stores Finance, driving AI/ML innovations in financial analytics. The leader builds and directs a multidisciplinary team to deliver scalable solutions, translating AI capabilities into production systems. The role requires strategic vision and execution excellence to transform finance operations, automate workflows, and improve forecasting and controllership through agentic AI, ML, and generative AI.

Senior Applied Scientist role at Amazon's FinTech organization, focusing on building and scaling generative AI applications and autonomous agents for financial operations. The role involves developing systems that process financial transactions, extract intelligence from documents, and power agents that learn from customer interactions. Key responsibilities include ensuring AI systems are trusted for compliance, designing agents that improve with user feedback, optimizing inference at scale using tiered models and LLMs, and developing robust evaluation frameworks. The position emphasizes shipping production-ready models, working across the full stack, and solving complex real-world financial problems.

This role focuses on developing advanced robotics systems that integrate AI, control systems, and mechanical design for automation. The scientist will define the scientific roadmap for whole body control and dexterous manipulation, applying deep learning and LLMs to solve complex operational challenges in dynamic environments. The role involves research and practical implementation of AI in physical robotic hardware, with a focus on shipping these systems.

Senior Manager to lead an AI-first security technology organization, owning the enterprise AI/ML roadmap, leading a team of scientists and BIEs, and delivering production AI/ML models for efficiency gains and loss reduction. The role involves establishing AI/ML delivery standards, building MLOps infrastructure, and partnering with business and technical leaders, while ensuring responsible AI and compliance with regulations.

This role is for a Sr. Applied Scientist on an Amazon Special Projects team focused on creating new products and services. The role involves leading research projects from ideation to production, driving ML/AI strategy, collaborating cross-functionally, publishing findings, and establishing best practices for ML experimentation and deployment. Requires a PhD or Master's with significant applied research experience, strong programming skills, and experience with ML/LLM fundamentals and deploying ML systems at scale. Experience with autonomous AI frameworks and translating research into production systems is preferred.

This role focuses on designing and implementing innovative AI solutions, developing ML models and frameworks, enabling self-service automation, and building evaluation frameworks to enhance productivity and unlock new value within Audible. The role involves applying ML/AI approaches to solve complex real-world problems and building the blueprint for how Audible works with AI.

Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, conducting data analysis, and collaborating with cross-functional teams to improve translation accuracy and efficiency for millions of customers worldwide.

Principal Applied Scientist role focused on AI-powered design automation for AWS data centers. The role involves defining research roadmaps, developing and deploying ML models (including fine-tuning foundation models, GNNs, NLP, RL, CV) for BIM and AECO applications, and publishing research findings. It requires a blend of theoretical ML knowledge and practical application in a domain with high trust requirements.

Software Development Engineer role focused on building the platform for validating safety-critical autonomous systems. This involves designing scenario generation pipelines, integrating generative AI models for realistic behaviors, creating synthetic sensor data, and developing export connectors for simulation platforms. The role spans the full lifecycle from data curation to deployment monitoring, with a focus on automating testing and exploring edge cases.

Senior Applied Scientist role focused on improving advertising performance and delivering innovative advertising experiences for Amazon devices and grocery. The role involves building and deploying machine learning models, with a specific emphasis on agentic AI for ads targeting, including autonomous agents, multi-agent orchestration, large multimodal models, reinforcement learning, and sequential decision making. The position requires experience in developing scalable data pipelines, optimizing conversion KPIs, and staying updated with the latest advancements in ML, NLP, and multimodal learning.

Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. This involves selecting, fine-tuning, and deploying models, identifying use cases, and providing technical guidance on responsible AI adoption. The role requires experience with ML/statistical modeling, software engineering best practices, and a Top Secret security clearance.

Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage.

This role focuses on advancing knowledge graphs for the LLM era, specifically for LLM grounding and construction pipelines. It involves web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory over graphs. The primary responsibility is entity resolution for conflating facts from multiple sources into a single graph entity, requiring scalable, generic, and streaming data solutions. The role also touches upon agent memory, suggesting a secondary stage involvement.

Machine Learning Engineer on AWS Professional Services team, focusing on designing, implementing, and scaling Generative AI solutions for customers. Requires TS/SCI clearance.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery of solutions impacting international customers.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

This internship focuses on research in Reinforcement Learning and Optimization within Machine Learning, developing and implementing novel algorithms for complex real-world challenges. The role involves working with large-scale data and applying cutting-edge ML techniques.

This internship focuses on developing systems and frameworks for machine learning asset lifecycle management, leveraging NLP and information retrieval. The role involves research into ML operations and knowledge engineering to enhance Amazon's ML capabilities.

Applied Scientist II role focused on building AI-based automated customer service solutions using RAG, agentic AI, and post-training of LLMs. Responsibilities include designing and deploying RAG pipelines, conducting LLM post-training, curating datasets, implementing evaluation frameworks, developing AI agents, and collaborating with cross-functional teams. The role involves research and development with minimal guidance, aiming to translate research into production systems and contribute to the scientific community.

The Data Scientist II will focus on developing evaluation and benchmarking datasets for enterprise AI features, specifically for Amazon Quick Suite. This involves leveraging Generative AI techniques, LLMs for synthetic data generation, and LLM-as-a-judge settings to assess model performance, ensure data quality, and contribute to Responsible AI initiatives. The role also includes building scalable data pipelines and tools for continuous evaluation.

Applied Scientist role focused on building and deploying AI/ML models for customer behavior prediction and personalization within Amazon Prime. The role involves working with large-scale data, leveraging GenAI, LLMs, deep learning, and reinforcement learning, and contributing to production AI/ML systems. Emphasis on scientific research, publication, and utilizing AWS technologies.

Applied Scientist role focused on developing and customizing large language models for enterprise use cases, involving techniques like supervised fine-tuning, reinforcement learning, and knowledge distillation. The role requires building enterprise-ready tooling, optimizing models, and contributing to responsible AI toolkits.

Applied Scientist focused on developing learning-based approaches for mobile manipulation in robotics, aiming to advance capabilities for robots navigating and manipulating objects in dynamic fulfillment environments. The role involves model development, training, data management, experimentation, validation, and code development for production systems at Amazon's scale.

This role focuses on accelerating customer adoption of Amazon Connect's AI capabilities by acting as an Applied AI Solutions Architect. The architect will guide customers in selecting foundation models, designing and optimizing AI prompts, and architecting tool integrations for agentic AI systems. A key aspect is ensuring customer data readiness for AI agents and helping customers move from proof-of-concept to pre-production for Amazon Connect + Unlimited AI deployments. The role involves hands-on coding, building integrations, configuring agents, and collaborating with customer engineering teams.

This internship focuses on developing and implementing cutting-edge computer vision algorithms and models for Amazon's consumer-facing products and services, such as Rekognition, Go, and Visual Search. The role involves working with large-scale systems, including mobile robots and advanced tooling, to solve real-world problems. Interns will contribute to production-level projects, technical white papers, and roadmaps, with a strong emphasis on applied science and deep learning in computer vision, potentially involving Vision-Language Models and LLMs.