This role focuses on developing novel frameworks and techniques for multi-object tracking, re-identification, person activity understanding, and multi-modal foundation models within the context of Amazon's Just Walk Out technology. The scientist will advance the theory and practice of these areas, create efficient visual processing techniques, and reduce computational/data requirements for visual AI systems. The role requires a strong publication record in top-tier conferences and experience in computer vision, deep learning, and multi-modal foundation models.

Lead a new applied science organization focused on building agentic AI systems for advertising campaigns. This role involves defining the scientific vision, research agenda, model architectures, and evaluation frameworks for LLM-based agents, multi-step planning, tool use, RAG, and RLHF, with a focus on delivering measurable value and transforming the advertiser journey. The role requires building and mentoring a team, partnering with cross-functional teams, and driving execution from research to production at scale.

Lead product vision and roadmap for Amazon Seller Assistant, a GenAI-first, multi-agent system. Responsible for defining innovations and scaling agentic capabilities for millions of sellers, collaborating with scientists and engineers to launch production-grade multi-agent systems at Amazon's scale. Also responsible for building and scaling industry-leading evaluation infrastructure.

Applied Scientist role focused on developing novel multi-modal generative AI agentic architectures and models for advertising creatives, integrating and deploying ML projects, curating datasets, and performing analysis. The role involves research, publication, and collaboration with cross-functional teams.

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 Principal Scientist role focused on developing industry-leading Agentic AI solutions for AWS, including autonomous agents for incident detection/resolution and proactive code repair, leveraging advanced deep learning and foundation models for cloud observability and security.

Applied Science role focused on developing foundational capabilities for useful AI agents, leveraging large vision language models (VLMs) with reinforcement learning (RL) and world modeling. Responsibilities include model training, dataset design, and pre- and post-training optimization in an applied research setting.

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.

Research role focused on developing foundational capabilities for AI agents, combining LLMs with reinforcement learning for reasoning, planning, and world modeling in virtual and physical environments. Involves model training, dataset design, and pre/post-training optimization.

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.

Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems.

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.

Senior Applied Scientist role focused on AI research and development, including Generative AI, Agentic AI, LLMs, and Diffusion Models for Amazon's catalog systems. The role involves designing, training, and deploying AI solutions, with a focus on scaling models and integrating them into production.

Senior AI/ML researcher to define ML strategy for a healthcare foundation model and inference system, focusing on frontier models, proprietary domain models, and monetizable features under regulatory constraints. Requires expertise in training/adapting large models, distributed training, RLHF/DPO, retrieval, evaluation, and ML systems engineering, with experience in high-stakes/regulated domains.

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.

Internship role focused on developing novel algorithms at the intersection of LLMs and generative AI for robotics, involving research in perception, manipulation, and control. Requires strong ML/DL/robotics background and publication record.

This internship focuses on developing novel algorithms and modeling techniques at the intersection of LLMs and generative AI for robotics, tackling research problems in robotic perception, manipulation, and control. The role involves collaboration with cross-functional teams and requires a strong background in machine learning, deep learning, and/or robotics, with a publication record at top conferences.

This internship focuses on developing novel algorithms at the intersection of LLMs and generative AI for robotics, involving research in robotic perception, manipulation, and control, with an emphasis on multimodal models and vision-language-action systems.

Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems.

Seeking an Applied Scientist to develop industry-leading technology with LLMs and multimodal systems, focusing on advanced approaches, model-in-the-loop and human-in-the-loop for high-quality data collection and LLM training, and enhancing customer experiences.

Research intern role focused on developing foundational capabilities for AI agents, combining LLMs with RL for reasoning, planning, and world modeling. The role involves running experiments, building tools to accelerate research workflows, and scaling AI systems within a fast-paced, iterative research lab environment.

Computer Vision Scientist role focused on developing and evaluating generative AI models for e-commerce media content, leveraging large datasets and cloud resources. The role involves research, implementation of novel ML techniques, and communication with stakeholders.

Research role focused on developing foundational capabilities for AI agents that can act in digital and physical worlds, with a focus on multimodal LLMs, automation agent systems, and applying GenAI to real-world problems. Involves rapid invention, experimentation, and collaboration.

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.

Principal Applied Scientist role focused on computer vision and multimodal LLMs, involving research, algorithm development, and translating research into practice for consumer products. Requires PhD, 10+ years of ML experience, and expertise in computer vision, VLM, and deep learning. The role involves defining research directions, developing long-term strategies, and mentoring junior scientists.

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 Manager of Applied Science to lead science and data teams working on innovative AI-powered supply chain solutions, focusing on GenAI/Agentic AI for enterprise applications. The role involves driving technical vision, fostering innovation, leading researchers, and delivering solutions to production.

Senior Principal Scientist role focused on pioneering AI technologies for secure enterprise collaboration, including novel AI architectures, human-AI interaction, AI agent orchestration, and privacy-preserving ML. The role involves translating business requirements into AI deliverables, inventing new product experiences, and bringing state-of-the-art LLM/GenAI models to production, while defining long-term science vision and collaborating with academic partners.

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.

Internship role focused on developing novel algorithms and modeling techniques at the intersection of LLMs and generative AI for robotics, tackling research problems in robotic perception, manipulation, and control. Involves collaboration with cross-functional teams and leveraging expertise in deep learning, reinforcement learning, computer vision, and motion planning.

Lead a research team focused on advancing foundational capabilities for useful AI agents by combining LLMs with RL. The role involves managing research, aligning roadmaps, mentoring, and hiring, with a focus on evolving agents for reasoning, planning, and world modeling. Experience with training large models, scaling foundational models, and applying post-training techniques is required.

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.

Research scientist role focused on state-of-the-art LLM technologies, integrating structured and unstructured information (e.g., RAG) for applications across Amazon businesses, with a focus on delivering innovations from research to production.

Lead the development of breakthrough foundation models for robotics, focusing on perception, manipulation, and interaction with the world. This role involves hands-on research, algorithm design, and scaling models for real-world deployment at Amazon scale, with a focus on multi-modal and efficient architectures.

Applied Scientist II role focused on developing and evaluating LLMs and multimodal systems for Alexa's international products. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs (SFT, DPO, RLHF, RLAIF), setting up experimentation, and contributing to research and production delivery. Requires strong ML, NLU, LLM architecture, and evaluation knowledge, with a focus on international customer nuances and diverse data sources.

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.

The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research.

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.

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.

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.