Applied Scientist role focused on building and productionizing agentic AI systems for trustworthy shopping experiences. The role involves multi-step reasoning, autonomous task execution, multimodal understanding, and leveraging techniques like fine-tuning and reinforcement learning to automate complex investigation processes at Amazon scale. It spans from research and experimentation to writing production code and evaluating models.

Manager for a Research Analysis team focused on foundational ML research and developing scalable ML solutions for customer experience and selling partner experience. The role involves architecting large-scale AI/ML systems, leading initiatives on LLM Agents, RAG, inference optimization, and evaluating model safety and fairness. The manager will also define AI strategy and mentor the team.

This role focuses on designing, developing, and deploying intelligent navigation systems for advanced robotic systems. It involves leveraging machine learning, AI, and control theory to create scalable and safe navigation solutions for dynamic environments. The role bridges research and production, with a strong emphasis on learning-based approaches, foundation models for embodied agents, and control-theoretic methods like MPC. Key responsibilities include developing perception algorithms, leading research in computer vision and sensor fusion, and owning ML models end-to-end, from data to deployment. The role also involves publishing research and mentoring junior scientists.

Applied Scientist role focused on building agentic AI systems for Amazon's Trustworthy Shopping Experience (TSE) team. The role involves developing multi-step reasoning, autonomous task execution, and multimodal intelligence, with a focus on automating complex manual investigation processes. Responsibilities include designing and implementing agentic AI solutions, productionizing models using various fine-tuning approaches, building deep learning and ML solutions, and prototyping rapidly. The role emphasizes end-to-end AI development from research to production, with contributions serving millions of customers.

Senior Applied Scientist focused on designing, developing, and deploying intelligent navigation systems for advanced robotic systems. This role involves leading research in learning-based planning and control, foundation models for embodied agents, and control-theoretic approaches like MPC, with a strong emphasis on translating research into deployed, scalable systems.

Senior Applied Scientist role focused on designing, developing, and evaluating long-running AI agents for AWS Applied AI Solutions. The role involves building agentic use cases, defining evaluation frameworks for complex agent outputs, and ensuring production deployment. Requires experience in building ML models for business applications and applied research.

Data Scientist role focused on building and shipping multi-agent AI systems for Amazon sellers, involving reasoning, planning, memory, and context engineering. The role requires defining product vision, translating research into features, and designing evaluation frameworks for agent quality and business impact.

Software Engineer role focused on optimizing collective operations for AWS Trainium, a purpose-built AI training chip. The role involves enhancing collective algorithms and topologies, optimizing compute for specific LLM training topologies, and working closely with hardware teams to maximize performance using C/C++. The goal is to scale AI compute across the data center for training frontier AI models.

Senior Applied Scientist role focused on developing and deploying state-of-the-art perception algorithms for advanced robotic systems. The role involves research in computer vision, sensor fusion, and 3D perception, with a strong emphasis on bridging theoretical research with real-world impact. Responsibilities include end-to-end ownership of ML models, from data to deployment, and publishing research findings. The role operates at the intersection of deep learning, LLMs, and robotics, aiming to enable seamless interaction between users, robots, and their environment.

Data Scientist role focused on building and shipping multi-agent AI systems for Amazon sellers, involving reasoning, planning, memory, and context engineering. The role requires defining product vision, translating research into features, and designing evaluation frameworks for agent quality and business impact.

Senior Applied Scientist role focused on building agentic AI systems, multi-agent architectures, tool-augmented LLMs, and RAG pipelines for climate-related products. The role involves end-to-end product development from research to production, with a focus on autonomous analysis, planning, and execution of recommendations, leveraging multimodal AI and deep learning on time series data.

Senior Applied Scientist role focused on advancing conversational AI technologies, specifically LLMs and generative AI, for Alexa. The role involves defining science roadmaps, architecting agentic systems, establishing evaluation frameworks, and driving end-to-end delivery of research initiatives from experimentation to production. Emphasis on building scalable agentic systems for conversation understanding and generation, and contributing to the team's scientific reputation through publications and patents.

Applied Scientist II role focused on designing, developing, and deploying data-driven models for ML and NL applications, with a strong emphasis on generative AI, NLP, and large-scale model training and deployment. The role involves researching and implementing novel ML approaches, fine-tuning foundation models, developing custom algorithms for model optimization, and conducting applied research on generative AI architectures and training strategies. Mentoring junior scientists is also a key responsibility.

Seeking Principal Engineers to join AWS Agentic AI organization. This role involves designing, building, and scaling systems for AI agent platforms, services, and tools, focusing on multi-agent workflows and foundational technical infrastructure. The position requires strong technical leadership, architectural design, and the ability to drive innovation in a fast-paced environment.

Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models on large datasets, optimizing for inference at scale, and collaborating with science and engineering teams for production deployments. It requires guiding technical direction, mentoring, and maintaining individual contributions.

Applied Scientist role focused on building and deploying production-grade, multi-agent generative AI systems for Amazon's Seller Assistant, impacting millions of sellers worldwide. The role involves creating next-generation tools, designing and deploying innovative models, and establishing scalable processes for model implementation and validation.

This role is focused on developing foundational capabilities for AI agents, combining LLMs with RL for reasoning, planning, and world modeling in virtual and physical environments. The role involves maintaining a task management system to support data and reliability improvements, with a focus on building the agent system from the ground up.

Senior Software Engineer role focused on developing, enabling, and optimizing large-scale ML model training (pre-training and post-training) on AWS Trainium accelerators. This involves working with distributed training frameworks, mixed-precision techniques, and performance tuning across various model families including LLMs, multimodal models, and RL workloads.

This role focuses on developing and implementing advanced visual reasoning systems and autonomous AI agents that understand complex spatial relationships, object interactions, and customer behavior patterns in real-time retail environments. It involves working at the intersection of computer vision and large language models to advance state-of-the-art visual AI.

Senior Applied Scientist role focused on building AI safety systems for Alexa, involving LLM evaluation, agentic AI for safety, and ensuring trustworthy AI responses. The role emphasizes developing responsible AI solutions at scale, with a focus on protecting customers and defining industry standards.

This role focuses on building industry-leading Agentic AI systems, including models, infrastructure, and applications, within AWS. The scientist will contribute to advancements in NLU, AI-assisted code development, reasoning with LLMs, LLM training/fine-tuning, and applied ML, impacting millions of customers through AI-powered products and services. The role involves developing technical breakthroughs, mentoring junior scientists, managing a small team, defining technology strategy, prototyping, and communicating R&D progress.

Develop training infrastructure for large-scale reinforcement learning on LLMs, working across the technology stack including ML systems, orchestration, and data management. Analyze, troubleshoot, and profile ML systems, and conduct MLSys research for new techniques and tooling.

Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models at scale, optimizing for inference, collaborating with other teams, guiding technical direction, and mentoring junior scientists. It spans the full spectrum from data preparation to model training, evaluation, and inference.

Senior Software Engineer role focused on developing, enabling, and performance tuning distributed training solutions for large-scale ML models (LLMs, Stable Diffusion, ViT) on AWS Neuron accelerators using PyTorch. The role involves building distributed training support into PyTorch, the Neuron compiler, and runtime stacks, with a focus on strategies like FSDP, PP, and Context parallel. Experience with post-training strategies is a plus.

Senior Software Engineer role focused on the distributed training of large-scale ML models (LLMs, Stable Diffusion, ViTs) on AWS custom silicon (Trainium, Inferentia). Responsibilities include leading efforts to build distributed training support in PyTorch and JAX, optimizing models for performance and efficiency, and working with chip architects and compiler engineers. Requires strong software development skills, ML foundation, and experience with distributed training libraries.

Senior Applied Scientist role focused on developing and implementing novel foundation models for logistics, combining multimodal data (image, video, geospatial) and large-scale training/inference. The role involves guiding technical direction, mentoring, and collaborating across science and engineering teams to deploy these models for various Amazon delivery use cases.

Principal Applied Scientist role focused on developing and implementing novel foundation models for Amazon's delivery logistics. The role involves designing deep learning architectures, training models on vast datasets, and ensuring production-level performance for multimodal data (image, video, geospatial). It emphasizes scientific leadership, collaboration, and mentoring, with a strong focus on both research and engineering aspects of foundation model development and deployment at scale.

Director of Applied Science to define and execute a unified science strategy for Amazon Connect's native AI features, focusing on generative AI and weaving AI throughout customer experiences. This role involves leading an applied science team, collaborating across AWS organizations, and driving innovation to differentiate Amazon Connect.

Senior Software Development Engineer on the Applied AI team, focusing on Knowledge Work Automation. The role involves designing, building, and shipping multi-agentic systems and AI agents for internal Amazon workflows, leveraging LLMs and production-grade distributed software. Key responsibilities include architecting agentic AI systems, innovating with LLM techniques, shipping reusable primitives, and driving end-to-end delivery.

Senior Applied Scientist to lead the development of AI models for satellite manufacturing, transforming data into an intelligence system that improves how satellites are built. Focuses on AI-native workflows like non-conformance disposition, root-cause analysis, and predictive test optimization, influencing real-world manufacturing decisions.

Senior Software Engineer to build foundational systems and consumer-facing features for trustworthy AI experiences at scale. Focus on privacy-preserving AI, responsible AI frameworks, and accessibility. Key challenges include latency vs. privacy trade-offs, AI safety, ambient computing privacy, multimodal AI systems, and real-time evaluation.

Software Development Engineer II to design and build AI-powered advertiser controls, including bidding systems, agentic architectures, and experimentation systems for Amazon's Sponsored Products and Brands. The role involves owning platform capabilities, developing AI engineering infrastructure, interfacing agentic architectures, and designing experimentation systems. Requires strong software engineering skills, experience with Gen AI/LLMs, fine-tuning, RLHF, and RAG, with a focus on delivering customer-facing AI products.

Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. The role involves designing, developing, and evaluating deep learning models for NLP and recommendation systems, building ML pipelines, running A/B experiments, and deploying models to production. The team is greenfield, aiming for direct business impact and encouraging top-tier publications alongside production deployment.

Senior Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. The role involves designing, developing, and evaluating deep learning and GenAI models, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating with engineers for production deployment. The team is greenfield, aiming for direct business impact and encouraging both production deployment and top-tier publications.

Applied Scientist II at Amazon Alexa Ads focused on building Generative AI models for conversational ads and personalization. The role involves designing, developing, and evaluating deep learning and GenAI models, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating with engineers for production deployment. The team is greenfield, aiming to rethink ad ranking, pricing, and personalization for voice and screen surfaces, with opportunities for both shipping products and publishing research.

Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. Responsibilities include designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, building ML pipelines, running A/B experiments, and collaborating on production deployment.

Senior Data Scientist role focused on conversational AI, LLMs, NLP, and Generative AI for Alexa. The role involves defining strategy, leading initiatives from problem formulation to production, establishing evaluation frameworks, and driving consensus on agentic systems. It requires expertise in machine learning, generative AI, and computer vision, with a focus on delivering scalable and impactful solutions for millions of customers.

Senior Manager, Applied Science at Amazon Prime Video Advertising, leading a team to build and scale ML/AI solutions for advertising optimization, experimentation, and generative AI-powered ad creative generation. The role involves setting scientific vision, managing managers, and driving strategic initiatives in a rapidly growing business.

Applied Scientist II on Amazon's Trustworthy Shopping Experience (TSE) team, focusing on building and productionizing generative AI solutions for automating complex manual investigation processes at scale. The role involves designing and building agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, leveraging techniques like SFT, RFT, and few-shot approaches. The scientist will also work on prompt optimization, novel Finetuned transformer architectures, and identifying business problems to apply state-of-the-art LLM workflows. The role offers end-to-end ownership from research to production deployment, with a focus on impacting cost-of-serving customers while maintaining trust and safety.

Applied Scientist role focused on optimizing Generative AI models for edge devices, involving quantization, pruning, distillation, and fine-tuning. The role also requires understanding and inventing optimization techniques for custom ML hardware and collaborating with hardware architects and compiler engineers. The goal is to develop production-ready edge models and publish research findings.

This role focuses on designing and implementing generative AI solutions for government customers using AWS services. The Solutions Architect will act as a Subject Matter Expert, guiding customers and internal teams on leveraging AI for automation and cost reduction. The role requires deep technical experience in AI/ML and integrating these services into production applications.

This role focuses on designing and implementing generative AI solutions for government customers using AWS services. The Solutions Architect will act as a Subject Matter Expert, guiding customers and internal teams on leveraging AI for automation and cost reduction. The role requires deep technical experience in AI/ML and integrating these services into production applications.

Manager for a team building an agentic GenAI platform for AWS data center operations, focusing on LLM orchestration, agent frameworks, search/knowledge systems, and full-stack serverless engineering.

Applied Scientist II role focused on building AI-driven solutions for Amazon Travel & Events, leveraging Generative AI, LLMs, NLU, conversational AI, and Applied ML. Responsibilities include designing, developing, and evaluating ML models using GenAI, multimodal reasoning, and information retrieval for catalog understanding, applying VLMs and LLM-based approaches with fine-tuning and RAG, implementing model optimization techniques for efficiency, driving experiments, building ML pipelines, contributing to model reliability through interpretability and calibration, and collaborating with teams to translate business requirements into ML solutions. The role also involves staying current with research and co-authoring publications.

Senior Applied Scientist role focused on building scalable neuro-symbolic systems that fuse formal reasoning with GenAI and agentic AI for AWS customers, aiming to deliver reliable, verifiable outcomes and enhance features like hallucination detection and guardrails. The role involves end-to-end ownership from research to production, collaboration, and mentoring.

Senior Applied Scientist role focused on developing 3D physics-based simulation environments and tools for robotics, specifically for training large-scale machine learning models using reinforcement learning and synthetic data generation. The role involves establishing processes, building real-to-sim workflows, and minimizing sim-to-real gaps, with a secondary focus on enabling agentic systems through simulation.

The role focuses on building a personalized and context-aware agentic advertiser guidance system using LLMs and sophisticated tooling for Amazon Ads. It involves integrating LLMs with tools, operating across various advertiser-facing platforms, and delivering solutions through advanced agent architectures and model customization.

Software Development Engineer role focused on building an Agent platform using Generative AI and ML technologies for Amazon Ads. The role involves designing and developing scalable systems for personalized ad experiences, optimizing for cost and latency, and pioneering new approaches to conversational AI. The team owns the SPB Agent, which powers reasoning behind agentic experiences in Ads Console, Sales, and Seller Central.