Amazon has 1472 active AI-related job listings. The company is heavily focused on roles within the "agents" stage, which accounts for 38% of its AI hiring, followed by "application" at 26%. Engineering is the dominant function, with 1172 positions. Over the last 30 days, Amazon has added 667 new AI roles, representing a 74% increase compared to the previous 30-day period. Frequent tech tags include agent_orchestration, model_serving, and multimodal.
Currently tracking 1110 active AI roles, down 16% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $194k).
Amazon currently has 1573 active AI-related roles in our index. The most common open titles are: ML Data Associate-II (9), 2026 Applied Scientist Intern, Amazon University Talent Acquisition (8), AI Data Associate (Dutch) , Artificial General Intelligence Data Services (8), Software Development Engineer, AWS (8), Senior Delivery Consultant - Data , Professional Services, AWSI HCLS (7). Most positions are in Engineering and Research.
Amazon's active AI hiring is concentrated in: agents (41%), application (26%), serving infrastructure (13%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Amazon is hiring AI talent in: United States (1023 roles), Canada (59 roles), United Kingdom (47 roles), India (23 roles).
Job postings at Amazon most frequently mention: Machine Learning, Generative AI, Large Language Models (LLMs), Software Engineering, Agentic Systems.
In the past 30 days, Amazon has posted 696 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and scaling generative AI training infrastructure, specifically for LLMs. Responsibilities include designing and implementing stable and efficient training systems, scalable data infrastructure, and end-to-end RL post-training pipelines. The role involves collaborating with scientists and engineers to improve training efficiency, reliability, and optimize RL training stability and efficiency. It also includes building observability systems and contributing to system design and technical roadmaps for a unified LLM training platform. | Post-trainData | 9 |
| Member of Technical Staff, Multimodal Agents, AGI Autonomy Principal Engineer role in Amazon AGI Lab focused on building multimodal agents and the systems to run them reliably at scale. This role involves taking models from prototype to production, setting technical direction, and partnering with researchers to scale emerging VLM and agent ideas. It requires end-to-end ownership, from agent runtime to data management and value delivery. |
| AgentServe |
| 9 |
| Director, Applied Science, Full Funnel Advertising (FAIM) Director-level role leading a team to build and scale AI/ML-powered agentic systems for advertising, optimizing advertiser outcomes across the full funnel. Focus on production systems, multi-agent architectures, and innovation in AI-powered advertising solutions. | Agent | 9 |
| Member of Technical Staff, Multimodal Agents, AGI Autonomy Principal Engineer to lead technical direction for a frontier research and product team building multimodal agents for AGI. The role involves end-to-end ownership from research collaboration and novel architecture design to productionizing systems, defining agent runtimes, and ensuring reliable operation at scale. Responsibilities include creating research/engineering tooling, mentoring, driving technical reviews, and influencing broader AGI initiatives through reusable primitives and clear strategy. The role also emphasizes external representation via publications and open-source contributions. | AgentServe | 9 |
| Applied Scientist II, Perception Applied Scientist II in Robot Perception at Amazon, focusing on developing and deploying advanced perception algorithms for robotic systems. This role involves research in computer vision, sensor fusion, and 3D perception, with a strong emphasis on bridging research with real-world impact and end-to-end ML model ownership. The position requires a PhD, experience in building ML models for business applications, and a publication record in top-tier venues. The role contributes to the development of next-generation robotic systems that integrate AI, control systems, and mechanical design for automation. | ShipServe | 9 |
| Applied Scientist II, Visual Search Science Applied Scientist II role focused on building an AI-powered visual search experience. This involves designing and optimizing generative AI models for real-time image generation, developing multimodal retrieval systems to connect images to a large product catalog, and building LLM-based classifiers for intent detection and safety filtering. The role also includes advancing AI safety and conducting large-scale online experiments. | AgentServe | 9 |
| Senior Applied Scientist, Safe Locomotion, Compass Senior Applied Scientist role focused on developing and deploying safe legged locomotion algorithms for robots using Reinforcement Learning (RL), sim-to-real transfer, and integrating learned policies with model-based control. The role involves training policies for dynamic gaits, ensuring safety constraints, and collaborating with other robotics teams. | AgentData | 9 |
| Senior Applied Scientist Senior Applied Scientist role focused on developing and deploying perception algorithms for robotic systems, leveraging computer vision, sensor fusion, and Vision-Language Models (VLMs). The role involves end-to-end ownership of ML models, from data to deployment, and contributing to research that reshapes the field of robotics and AI. | Agent | 9 |
| Applied Scientist, Safe RL, Robotics, SAF Lab This role focuses on developing and deploying safe reinforcement learning (RL) policies for dynamic legged locomotion on physical robots. It involves creating RL architectures that interface with physics models, internalize safety constraints during training, and transfer policies from simulation to real-world hardware. The work sits at the intersection of safety-critical control and learning, aiming to enable robots to operate safely around humans. | ShipData | 9 |
| Applied Scientist, Navigation This role focuses on designing, developing, and deploying advanced navigation systems for robotic systems, leveraging cutting-edge AI, foundation models, and control-theoretic approaches. The scientist will lead research, own ML models end-to-end, and translate research into deployed production systems for autonomous robotics at scale. | AgentServe | 9 |
| Director, Applied Science, Alexa for Shopping (Rufus) Director of Applied Science for Alexa for Shopping, leading the science vision and execution for a next-generation conversational AI platform. This role involves owning the end-to-end science roadmap for a multi-agent architecture powered by LLMs, SLMs, RL, and post-training optimization to create a personalized and intelligent shopping assistant. The focus is on distilling data, building specialized models through fine-tuning and RL, and architecting intelligent agent routing. | AgentPost-train | 9 |
| Principal Solutions Architect, Generative AI, AWS Industries, Telco This role is for a Principal Solutions Architect focused on Generative AI within AWS Industries, specifically for the Telco sector. The primary responsibility is to design, architect, and deliver production-grade generative AI solutions, including agentic voice systems, AI assistants, and real-time translation. The role involves prototyping, building proof-of-concepts, writing code, contributing to open-source projects, and developing reusable blueprints. It requires deep expertise in generative AI, including foundation models, multimodal models, RAG, speech models, and agentic workflows, combined with an understanding of complex and regulated telco environments. The Solutions Architect will act as a trusted technical advisor to telco customers, guide their AI-native product design, influence AWS product roadmaps, and publish their work. The role emphasizes building AI products in complex, regulated environments and requires comfort with both high-level architecture and hands-on coding. | AgentServe | 9 |
| Applied Scientist II, Perception Applied Scientist II, Perception at Amazon, focusing on developing and deploying state-of-the-art perception algorithms for robotic systems. This role involves research in computer vision, sensor fusion, and 3D perception, with a strong emphasis on integrating deep learning, LLMs, and robotics to create intelligent automation solutions. The scientist will own ML models end-to-end, from data to deployment, and contribute to publications in top-tier venues. | ShipAgent | 9 |
| Sr Data Scientist, SPX AI Lab, SPX Science Senior Data Scientist role focused on defining and building agentic AI capabilities for Amazon Seller Assistant, a GenAI-first multi-agent system. The role involves owning the science vision, shipping agentic experiences, translating research into production features, and designing evaluation frameworks for a system used by millions of sellers. | Agent | 9 |
| Senior Applied Scientist, Selling Partner Support Engagement Senior Applied Scientist role focused on building and improving AI agents for customer support using reinforcement learning and agentic architectures. The role involves end-to-end research and development, from problem formulation to production deployment, with a focus on preference learning, reward modeling, and policy optimization for conversational agents. It also includes building evaluation frameworks and collaborating with engineering teams to deploy models at scale. The role emphasizes shipping AI agents that autonomously resolve issues and learn from interactions. | AgentPost-train | 9 |
| Principal Applied Scientist, AWS Agentic AI Principal Applied Scientist role at AWS focusing on Agentic AI for an enterprise generative AI assistant. Responsibilities include leading research and development in generative AI and Agentic AI, building and optimizing multi-modal foundation models, training and fine-tuning LLMs, and architecting scalable systems. The role involves bringing research into production and enabling intelligent agents for complex reasoning and workflow automation. | AgentPost-train | 9 |
| Sr. Applied Scientist, Amazon Cyber Threat Intelligence Senior Applied Scientist role focused on inventing and deploying AI/ML systems for cyber threat intelligence at Amazon scale. Responsibilities include identifying and solving complex threat intelligence problems, extending ML techniques for cybersecurity, and implementing production AI/ML systems for threat detection, analysis, and defense. The role involves building predictive models, graph neural networks, and LLM-powered systems, with a strong emphasis on deploying models into production and influencing across teams. | AgentServe | 9 |
| Applied Scientist II, GenAI Evaluation Media (GEM) Applied Scientist II role focused on GenAI Evaluation Media (GEM) for visual shopping experiences. The role involves research and development of agentic AI capabilities for multimodal understanding, visual content generation/editing, virtual try-on, and automated quality assurance. Success requires establishing robust metrics, collaborating cross-functionally, and delivering scalable solutions. | AgentShip | 9 |
| Sr. Applied Scientist, AWS Just-Walk-Out Science Team Sr. Applied Scientist role on the AWS Just-Walk-Out Science Team, focusing on developing and implementing advanced visual reasoning systems and autonomous AI agents for checkout-free retail environments. This role involves tackling complex problems in computer vision, machine learning, and real-time systems, with a strong emphasis on innovation and pushing the state of the art. | AgentServe | 9 |
| Senior Applied Scientist This role focuses on developing and deploying ML-based perception systems for robots using radar and thermal imaging, fusing this data with traditional sensors to enable operation in challenging conditions. The primary output is the deployed perception system (L3), with significant work also in developing and refining the ML models themselves (L2). | ServePost-train | 9 |
| Applied Scientist II, AWS Just-Walk-Out Science Team The Applied Scientist II role on the AWS Just-Walk-Out Science Team focuses on developing and implementing advanced visual reasoning systems and autonomous AI agents for a checkout-free retail environment. This involves understanding complex spatial relationships, object interactions, customer behavior, and adapting to dynamic retail settings using computer vision, sensor fusion, and machine learning. | AgentServe | 9 |
| Member Of Technical Staff - Hardware Science, Frontier AI & Robotics (FAR) This role focuses on building and deploying intelligent robotic systems by developing foundation models for perception and manipulation, integrating them with hardware, and driving research from conceptualization to production at Amazon scale. It involves deep learning for physical systems, control algorithms, and collaboration with hardware engineering teams. | ShipPost-train | 9 |
| Senior Applied Scientist, Fauna Senior Applied Scientist role focused on developing and optimizing advanced AI/ML algorithms, particularly reinforcement and imitation learning, for robotic motor control systems. The role involves integrating these systems with hardware, using simulation and real-world testing, and leading projects from conception to production deployment, with a strong emphasis on sim-to-real transfer and robotics applications. | ShipAgent | 9 |
| Senior Applied Scientist, Funnel Agentic Intel This role focuses on building and evaluating agentic AI systems for Amazon Ads. The agent will understand advertiser intent, reason about campaign strategy, and execute actions across the Amazon Ads portfolio. Key responsibilities include designing and building multi-step agentic workflows, invoking tools, and taking autonomous actions. The role also involves defining evaluation frameworks for agent reliability, correctness, and safety, analyzing agent behavior through data analysis and A/B experimentation, and partnering with cross-functional teams to ship end-to-end agent experiences at scale. | Agent | 9 |
| Applied Scientist, Navigation 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. | AgentServe | 9 |
| Senior Applied Scientist, Navigation 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. | AgentServe | 9 |
| Sr. Applied Scientist, Applied AI Solutions 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. | Agent | 9 |
| Software Development Engineer, Neuron Collectives, Annapurna Labs 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. | Data | 9 |
| Senior Applied Scientist, New Initiatives 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. | Agent | 9 |
| Senior Applied Scientist , Alexa AI Aurora 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. | AgentEval Gate | 9 |
| Principal, Senior Principal and Distinguished Engineer, AWS Agentic AI 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. | Agent | 9 |
| Applied Scientist, SPX AI Lab 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. | AgentShip | 9 |
| Applied Scientist II, AWS Just-Walk-Out Science Team 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. | AgentPost-train | 9 |
| Member of Technical Staff - Reinforcement Learning (Infrastructure), AGI Autonomy 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. | DataAgent | 9 |
| Senior Applied Scientist, Delivery Foundation Model 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. | Post-trainServe | 9 |
| Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training 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. | PretrainPost-train | 9 |
| Sr. Applied Science Manager, Perfect Order Experience (POE) AI Senior Applied Science Manager leading a team to develop a domain-specific LLM, including pre-training, fine-tuning, and reinforcement learning. The role also involves architecting risk detection systems using multi-modal signals and influencing ranker models for product visibility. The focus is on building and scaling AI solutions for Amazon's Perfect Order Experience. | Post-trainPretrain | 9 |
| Delivery Consultant- AI/ML, WWPS ProServe This role focuses on designing, implementing, and scaling AI/ML solutions for AWS customers, with a strong emphasis on generative AI. The consultant will work with customers to identify use cases, select, train, and fine-tune models, and deploy these solutions. The role involves architecting complex solutions, conducting experiments, and optimizing performance, acting as a trusted advisor on AI/ML advancements. | AgentPost-train | 8 |
| Sr. Software Engineer, Alexa Connections This role focuses on designing and developing large-scale agentic AI systems for Alexa's communication features, emphasizing real-time solutions and end-to-end data excellence within a consumer product context. | Agent | 8 |
| Senior Data Scientist, Enterprise Security Products Senior Data Scientist role focused on building AI-first security products, with a strong emphasis on agentic AI, RAG, and evaluation frameworks. The role involves setting science strategy, architecting solutions, and driving adoption of AI tooling within an enterprise security context. | Agent | 8 |
| Software Development Engineer, Catalog Diagnostics (Agentic) & Analytics Software Development Engineer role focused on building agentic diagnostics and analytics services for Amazon's product catalog. The role involves using Generative AI, VLMs, and multimodal reasoning to understand product identity and relationships at a massive scale. Responsibilities include designing and implementing agentic frameworks, pioneering explainable AI, owning data pipelines, and defining product roadmaps. The team manages large data lakes and builds agentic solutions for catalog diagnostics, focusing on feedback loops, accuracy, and automated skill creation. | AgentData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II focused on building and optimizing generative AI training systems, specifically for LLMs and RL post-training pipelines, at Amazon's Stores Foundational AI team. The role involves designing scalable data infrastructure, improving training efficiency and reliability, and translating research algorithms into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II focused on building generative AI for shopping experiences at Amazon. The role involves designing and implementing stable and efficient training systems for model training and reinforcement learning, collaborating with applied scientists and engineers to improve training efficiency, and developing scalable data infrastructure for Amazon-scale data ingestion and processing across training and evaluation stages. Requires experience with ML and LLM fundamentals, transformer architecture, and training/inference lifecycles. | DataPost-train | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II at Amazon on the Stores Foundational AI team, focusing on building and optimizing large-scale LLM training infrastructure, including pretraining and RL post-training pipelines, data infrastructure, and observability systems for generative AI in shopping. | Post-trainData | 8 |
| Sr Software Dev Engineer, Stores Foundational AI -SFAI Senior Software Development Engineer focused on building and scaling ML infrastructure for foundational LLMs in Amazon Stores, specifically involving RL post-training pipelines, stability, efficiency, and translating research into production systems. | Post-trainServe | 8 |
| Sr Applied Scientist Sr. Applied Scientist at Amazon Ring focused on computer vision and machine learning to improve customer experience and neighborhood safety. The role involves developing and deploying data-driven models, researching state-of-the-art algorithms, and collaborating with product and engineering teams. Requires experience with Multi-modal LLMs/Vision Language Models and experience in building ML models for business applications. | ShipPost-train | 8 |
| Applied Scientist II, Demand Science Applied Scientist II role focused on developing and deploying state-of-the-art ML/AI solutions for demand forecasting and supply optimization for Amazon Devices. The role involves full lifecycle ownership from research to production, including building forecasting models, exploring data, developing new approaches with AI-native experimentation and agent-driven automation, and partnering with engineering for deployment. Experience with transformer architectures, LLM-powered agents, and deep learning is required. | ShipAgent | 8 |
| Senior Manager Software Development, AWS Systems Manager Senior Manager Software Development role leading three engineering teams in AWS Systems Manager's Automation suite. The role focuses on building the execution layer for AI agents to safely take actions on cloud infrastructure at scale, including pre-execution impact analysis, safety frameworks, and intelligent orchestration. The teams will ship capabilities that enable customers to trust AI agents for autonomous operations. | AgentServe | 8 |
