Currently tracking 1110 active AI roles, down 16% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $194k).
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.
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 |
| Applied Scientist II, Alexa for Shopping Science UK Applied Scientist II role focused on developing and optimizing LLM/SLM powered conversational experiences for Alexa Shopping. This involves designing and implementing LLM agents, instruction design, contextual grounding, using MCP tools, agent/multi-agent systems, context engineering, model fine-tuning, and evaluation frameworks. The role also involves applying ML/DL techniques for last-mile improvements in ranking, relevance, personalization, and multimodal understanding, and designing agentic architectures with considerations for quality, latency, and reliability at scale. It requires hands-on analysis of multimodal interaction datasets, using statistical methods for evaluation and optimization, and collaborating with product and engineering teams. | AgentPost-train | 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, EU INTech Consumer Selection Discovery, NintAI Applied Scientist role focused on building and deploying AI/ML models for Amazon's global search and discovery experiences, with an emphasis on computer vision, generative AI, recommendation systems, and ranking. The role involves end-to-end ownership from problem analysis to production deployment, aiming to improve customer navigation and product discovery. | Ship | 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. Prototyping Architect, AWS Prototyping and AI Customer Engineering (PACE) This role focuses on architecting and building Generative AI and Agentic AI prototypes for AWS customers, leveraging LLMs, RAG, function calling, autonomous agents, and multi-agent orchestration. The emphasis is on rapid development and demonstrating the potential of these technologies to influence customer adoption. | Agent | 9 |
| Senior Applied Scientist, AGI Customization Senior Applied Scientist role focused on developing state-of-the-art services and tools for model customization (fine-tuning, RL, knowledge distillation) for Amazon Nova, enabling enterprises to build application-specific models. | Post-trainPretrain | 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 |
| ML Engineer, Fauna Machine Learning Engineer to train, evaluate, and deploy models for robots, focusing on reinforcement learning, computer vision, and supervised learning for embodied systems. Responsibilities include training policies, debugging convergence, running experiments, optimizing models for edge hardware, and building MLOps infrastructure. | Post-trainServe | 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 Sciences Manager , Ads Brand Safety and Suitability Manager for an Applied Sciences team focused on building AI-powered Brand Safety and Content Classification systems for Amazon Ads. The role involves leading the development of next-generation systems that make millisecond-level decisions across billions of content signals, adapting to emerging risks from generative AI. Key challenges include detecting AI-generated content, understanding contextual brand risk, designing adaptive models, and leveraging LLMs for real-time semantic understanding at internet scale. | AgentServe | 9 |
| Software Engineer I - AI/ML, AWS Neuron Distributed Training Software Engineer role focused on developing, enabling, and optimizing large-scale ML model training (pre-training and post-training of LLMs, multimodal, and RL workloads) on AWS Trainium accelerators. This involves working with distributed training frameworks, mixed-precision techniques, and performance tuning on specific hardware. | PretrainPost-train | 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 |
| Applied Science Manager, Alexa Edge AI Manager for a new Alexa Edge AI team in Bangalore, focused on developing and deploying on-device ML models for computer vision, acoustic modeling, and multimodal understanding to power Alexa devices. The role involves building and leading a team, driving R&D for privacy-preserving edge solutions, optimizing for resource-constrained hardware, and collaborating with hardware/silicon teams. Emphasis on end-to-end lifecycle ownership, from research to production deployment at scale, with a focus on latency, privacy, and accuracy. | ServePost-train | 9 |
| Applied Scientist II, Sponsored Products Autonomous Campaigns The Applied Scientist II will pioneer agentic AI applications for Amazon advertisers, designing agentic architectures, developing tools and datasets, and building autonomous systems for campaign workflows. This role involves fine-tuning, reinforcement learning, preference optimization, and creating evaluation frameworks for safety and reliability. Responsibilities include designing and building agents, implementing optimization techniques, curating datasets, building evaluation pipelines with guardrails, developing agentic architectures with planning and tool use, and prototyping multi-agent orchestration. The role requires working independently on ambiguous problems and collaborating to bring innovations into production, staying current with LLM, RL, and agent-based AI research. | AgentPost-train | 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 |
| Senior ML Engineer, Fauna Senior ML Engineer focused on training, evaluating, and deploying models for robots, with expertise in reinforcement learning, computer vision, and supervised learning for embodied systems. Responsibilities include training policies, debugging convergence, running experiments, optimizing models for edge deployment, and building MLOps infrastructure. | Post-trainServe | 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 |
| Member of Technical Staff - Machine Learning, Frontier AI Robotics Leads an ML infrastructure team focused on creating model training and simulation environments for large robotics foundation models. This involves defining roadmaps, building realistic simulation environments for RL and synthetic data generation, and implementing tooling for data creation and experimentation. The role emphasizes large-scale training, multi-modal models, and robotics applications. | DataPretrain | 9 |
| Member of Technical Staff - ML Engineer, Frontier AI Robotics ML Engineer role focused on building and optimizing distributed training infrastructure for large-scale deep learning and transformer-based models, specifically for frontier AI robotics applications. The role involves working with scientists and engineers to deliver scalable, high-performance systems, leveraging PyTorch, Python, and C++, and optimizing GPU performance for training. | Data | 9 |
| Applied Scientist, Trustworthy Shopping Experience (TSE) 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. | AgentPost-train | 9 |
| Manager, Research Analysis, RBS Tech 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. | AgentServe | 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 |
| Applied Scientist, Trustworthy Shopping Experience (TSE) 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. | 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 |
| Data Scientist, SPX AI Lab, SPX Science 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. | 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 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. | AgentServe | 9 |
| Data Scientist, SPX AI Lab, SPX Science 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. | Agent | 9 |
| Applied Scientist II, AFT AI, Amazon AFT AI Applied Scientist II role focused on developing and deploying agentic AI solutions and multi-modal deep learning models for Amazon's Fulfillment Network. The role involves working with large-scale, real-world datasets (imagery, natural language, structured data) to solve complex problems like warehouse operations and visual defect detection, pushing the state-of-the-art in optimizing fulfillment systems. | AgentPost-train | 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 |
| Applied Scientist III, AFT AI, Amazon AFT AI Develop agentic AI and multi-modal deep learning models for Amazon's Fulfillment network, focusing on understanding warehouse operations and visual defect detection. This role involves working with large, diverse datasets and applying cutting-edge AI techniques to solve complex, real-world problems at scale, with a strong emphasis on production deployment and iterative improvement. | AgentPost-train | 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 |