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.

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.

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 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 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.

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 focused on optimizing and accelerating deep learning and GenAI workloads on AWS's custom ML accelerators (Inferentia and Trainium) through the AWS Neuron SDK. This role involves architecting, implementing, and tuning distributed inference solutions, focusing on performance optimization (latency and throughput) from system level to framework level (PyTorch, JAX). The engineer will work on low-level optimizations, system architecture, and ML model acceleration, collaborating across hardware, compiler, runtime, and framework teams.

Software Development Engineer role focused on building AI agents and tools to simplify and accelerate customer adoption of Amazon's AWS Neuron ML software stack, which supports Trainium and Inferentia ML chips. The role involves applying Generative AI to AI itself, identifying obstacles, and developing solutions to improve the porting and optimization of ML workloads on AWS ML silicon.

The Principal Applied Scientist will lead the development and deployment of generative AI technologies for Amazon Ads, specifically within the Sponsored Products and Brands team. This role involves reinventing advertising experiences by integrating AI into the ad lifecycle, from creation to performance analysis. The scientist will invent new product experiences, bring state-of-the-art GenAI models to production, and define the long-term science vision for the advertising business, collaborating closely with science, product, and engineering teams to deliver high-impact products.

Principal Applied Scientist for Amazon Payments AI/ML Team, focusing on AI services for payments, recommendation, prediction, and GenAI for SOP automation. The role involves setting AI direction, mentoring scientists, partnering with engineering to deliver ML/GenAI features, identifying research directions, creating roadmaps, and bringing research to production. Requires a PhD or MSc with 10+ years of experience, a track record of thought leadership, and strong collaboration skills.

Machine Learning Software Development Engineer focused on computer vision models for robotics applications within Amazon's fulfillment and delivery network. The role involves designing, building, and maintaining end-to-end ML solutions from data collection and training to deployment on edge devices, with a strong emphasis on operationalizing research models and ensuring model health in production.

Senior Manager of Applied Science at Amazon, leading a multidisciplinary team to build the next generation of personalized shopping experiences. The role involves developing state-of-the-art LLM-based techniques, deep learned transformer models for customer intent, and large-scale real-time multi-task ranking systems. The goal is to create AI primitive systems that empower other teams and directly impact millions of customers through personalized features.

Manager for a Continuous Model Evaluation and Learning workstream within Amazon Ads' Sponsored Products and Brands team. The role involves leading a team of applied scientists and engineers to build and ship an evaluation and remediation framework for an agentic brand-intelligence system. This includes designing evaluation metrics, developing optimization engines for prompts and synthetic data, and ensuring offline-to-online consistency for quality improvements. The goal is to enable autonomous detect-diagnose-remediate loops to scale quality across brand skills.

Data Scientist II role focused on building AI-first security products, including designing, training, and shipping ML models for agentic systems, anomaly detection, and threat classification. The role involves owning the full ML lifecycle, using AI tools to accelerate development, and powering multi-agent security systems with RAG pipelines and evaluation frameworks. It emphasizes rapid prototyping, customer validation, and collaboration across disciplines.

Senior Security Engineer focused on offensive security operations and research for AI systems, including training pipelines, inference systems, and model architectures. The role involves discovering and exploiting vulnerabilities, developing automation for threat emulation, and collaborating with engineering teams to improve AI security posture.

Data Scientist II role focused on building AI-first security products, including agentic systems, anomaly detection, and threat classification. The role involves the full ML lifecycle, from problem framing to production deployment and monitoring, with an emphasis on using AI tools to accelerate development. Key responsibilities include powering agentic architectures with models, embeddings, RAG pipelines, and evaluation frameworks, rapid prototyping, and customer validation. The role also involves partnering across disciplines and communicating complex results. The team operates with startup speed at Amazon scale, emphasizing rapid iteration and shipping.

Scientist role focused on designing and developing machine learning solutions for customer behavior analytics, utilizing deep learning, LLMs, recommendation systems, and reinforcement learning. Key responsibilities include fine-tuning generative models, developing recommendation and decision models, building behavioral representations, applying post-training optimization, and creating evaluation frameworks. The role emphasizes measurable business impact and customer satisfaction.

Senior Software Engineer role focused on building AI systems for satellite manufacturing intelligence. The role involves architecting and implementing a platform that connects design, production, test, and quality data, utilizing AI-native workflows with retrieval systems, foundation models, agentic tool use, and human review. Key responsibilities include designing AI-native workflows, creating evaluation mechanisms for AI quality, and building production software. The role emphasizes building agentic systems (L4) with a strong focus on evaluation and quality gates (L5).

Senior Applied Scientist role focused on building AI-powered tooling for AWS Security operations, including generative AI incident response assistants, natural language-driven response, detection enrichment pipelines, and security data analytics platforms. The role involves defining and executing the ML/AI roadmap, extending and inventing techniques at the product level, and bringing models from research into production systems. Responsibilities include LLM-powered incident triage, anomaly detection, RAG, prompt engineering, fine-tuning, developing evaluation frameworks, and mentoring engineers.