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 |
|---|---|---|
| Senior Applied Scientist, AWS Quick Senior Applied Scientist role focused on building next-generation models for intelligent automation within AWS. The role involves designing and implementing neuro-symbolic systems that integrate formal reasoning with GenAI for reliable outcomes, enhancing formal reasoning capabilities for agentic applications, and driving adoption of these solutions across AWS services. It requires end-to-end ownership of the science lifecycle, including research, experimentation, production deployment, and defining performance metrics. The position also involves mentoring junior scientists and contributing to state-of-the-art through publications and patents. | AgentEval Gate | 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 |
| Robotics/AI Motor Control Scientist, Fauna Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots, focusing on creating and optimizing intelligent motor control strategies for complex, whole-body tasks. The role involves leveraging RL/IL, integrating with hardware, using simulation and real-world testing, and leading projects from conception to deployment within Amazon's Fauna Robotics team. | ShipData | 9 |
| Applied Scientist II, Foundation Model, Robotics This role focuses on developing and improving machine learning systems for advanced robotics, leveraging and adapting state-of-the-art foundation models, and inventing new algorithms. The primary output is agentic robotic systems, with a secondary focus on data and training workflows. | AgentData | 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 |
| Member of Technical Staff - Science, Frontier AI & Robotics (FAR) This role focuses on foundational research and building intelligent robotic systems, operating at the intersection of AI research and robotics. The individual will conduct original research, publish findings, and deploy innovations into production systems at Amazon scale. Key areas include developing foundation models, full-stack robotics systems, locomotion, manipulation, perception, sim2real transfer, multi-modal and multi-task robot learning, and designing frameworks that bridge research and deployment. | AgentPost-train | 9 |
| Member of Technical Staff - Hardware Science, Frontier AI & Robotics (FAR) This role focuses on foundational research and building intelligent robotic systems by developing foundation models for perception and manipulation, integrating them with hardware systems, and deploying them at Amazon scale. It involves independent research initiatives, full-stack robotics projects from conceptualization to hardware deployment, and collaboration with hardware engineering teams. | ShipData | 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 |
| Robotics/AI Motor Control Scientist, Fauna Robotics/AI Motor Control Scientist role focused on developing and optimizing ML algorithms, particularly reinforcement and imitation learning, for robot motor control. The role involves research, simulation, sim-to-real transfer, and integration with hardware, aiming to enable complex whole-body tasks and safe human-robot interaction. It bridges research with practical engineering and has a strong publication requirement. | ShipData | 9 |
| Applied Scientist, RL post-training, AWS Research scientist role focused on Reinforcement Learning (RL) post-training of frontier Large Language Models (LLMs) to improve capabilities like instruction following, reasoning over long context, and tool use for customer service applications within AWS. | Post-train | 9 |
| Member of Technical Staff, Frontier AI & Robotics (FAR) This role focuses on foundational research and building intelligent robotic systems, operating at the intersection of AI research and robotics. The individual will conduct original research, publish findings, and deploy innovations into production systems at Amazon scale. Key responsibilities include driving research initiatives across the robotics stack, designing novel deep learning architectures, guiding technical direction for full-stack robotics projects, and collaborating with platform and hardware teams. The role emphasizes developing breakthrough foundation models and full-stack robotics systems that enable robots to perceive, understand, and interact with the world. | AgentPost-train | 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 |
| Senior Applied Scientist, Alexa International Senior Applied Scientist role focused on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems, with an emphasis on multi-lingual applications across text, speech, and vision domains. The role involves driving scientific strategy, influencing partner teams, and delivering solutions impacting global customers. | Post-trainAgent | 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 |
| Senior Applied scientist, Agentic AI, AWS Agentic AI Research scientist role focused on building next-generation models for intelligent automation using autonomous agents, API orchestration, multimodal models, and reinforcement learning within AWS. | Agent | 9 |
| Applied Scientist, Agentic Automated Reasoning Group Pioneering next-generation neuro-symbolic tools by fusing AI breakthroughs with cloud scale and automated reasoning expertise. This role involves building scalable formal reasoning solutions, integrating GenAI and Agentic AI, and applying software engineering best practices to production systems. Responsibilities include defining and implementing automated reasoning features, designing and running RL pipelines, experimenting with model tradeoffs, and collaborating cross-functionally. The role also focuses on enhancing formal reasoning systems for GenAI applications, owning the science lifecycle, and advancing the state of the art through publications and patents. | 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 |
| Senior Applied Scientist, Shopping Core Foundations - BuyForMe This role focuses on building and researching autonomous AI agents for online shopping, operating on the open web. It involves LLMs, reinforcement learning, multimodal reasoning, and large-scale systems, with a focus on production-grade reliability, scalability, and safety. The scientist will design evaluation systems, develop agent planning and adaptation techniques, build multimodal reasoning systems, and lead scientific direction for agent reliability and customer trust. | AgentEval Gate | 9 |
| Applied Scientist - Agentic AI, Amazon Fulfillment Technology This role focuses on developing and researching agentic AI systems for operational decision-making and orchestration within Amazon's fulfillment network. It involves building full agentic systems using multi-agent orchestration, tool use, memory, and action execution, training LLMs through various methods including RL, and conducting rigorous evaluations. The role also includes leading research projects, mentoring, and publishing academic papers. | AgentPost-train | 9 |
| Applied Scientist Gen AI - Amazon Advertising, CreativeX Applied Scientist role focused on developing novel AI Agent architectures and multi-modal Generative AI models (audio, images, videos) for advertisers within Amazon Advertising's CreativeX team. The role involves research, development, and productionization of these models, with an emphasis on agent evaluation, LLM/VLM fine-tuning, and reinforcement learning. | AgentPost-train | 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 |
| Member of Technical Staff, Artificial General Intelligence Research role focused on developing foundational Generative AI (GenAI) technology using Large Language Models (LLMs) and multimodal systems, involving model training, dataset design, and pre/post-training optimization. | Post-trainPretrain | 9 |
| Member of Technical Staff - Science, Frontier AI & Robotics (FAR) Research role focused on developing foundation models for robotics, involving perception, manipulation, and multi-modal learning, with a goal of real-world deployment. | Post-trainAgent | 9 |
| Sr. Applied Scientist, Enterprise Security Products Senior Applied Scientist role focused on building AI-first security products. The role involves defining the science vision, inventing and building novel ML solutions (including agentic architectures and RAG systems), tackling ambiguous security challenges, and shipping end-to-end solutions. It requires staying ahead of advancements in foundation models and agentic AI, and influencing across the organization. The role emphasizes research rigor, rapid prototyping, and influencing the team's culture and scientific practices. | AgentPost-train | 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 |
| Senior Applied Scientist Senior Applied Scientist at Amazon focused on using Generative AI, VLMs, and multimodal reasoning to understand product identity and relationships within Amazon's catalog. The role involves formulating research problems, designing and implementing models for product relationship inference and catalog understanding, pioneering explainable AI, owning ML pipelines from research to production, defining research roadmaps, and mentoring peers. It emphasizes tackling ambiguous problems at scale, reasoning across text and images, and deploying solutions that impact millions of customers. | 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 |
| Applied Scientist, Trust CX Innovations&AI Policy Research-focused Applied Scientist role at Amazon working on generative AI for Alexa, focusing on LLMs, multimodal models, AI safety, alignment, and responsible AI. The role involves developing innovative solutions, optimizing models, evaluating performance, and leading the development of production-ready AI solutions, with a strong emphasis on research publications and patents. | Post-trainAgent | 9 |
| Principal Applied Scientist, Neuro-Symbolic AI Labs Research scientist role focused on building neuro-symbolic AI systems using proof assistants for complex problem-solving across various domains within Amazon. The role involves defining and implementing new applications, delivering scientific artifacts, and working in an agile environment. Requires a PhD or Master's with significant applied research experience, and experience leading scientists. | Agent | 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 |
| Applied Scientist II, Alexa International Applied Scientist II at Amazon Alexa International focusing on developing and applying LLMs and multimodal systems for multi-lingual applications. The role involves research, fine-tuning/post-training LLMs, building evaluation metrics, and driving scientific strategy from research to production, impacting global customers. | Post-trainShip | 9 |
| Applied Scientist, Alexa Connections Applied Scientist role focused on developing novel algorithms and modeling techniques for LLMs and multimodal systems within Alexa Connections. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs, setting up experimentation frameworks, and contributing to end-to-end delivery from research to production, with potential for publications. | Post-trainAgent | 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 |
| 2026 Fall Applied Science Internship - Gen AI & Large Language Models - United States, PhD Student Science Recruiting PhD internship focused on applied science in Gen AI and LLMs, involving fine-tuning models, developing novel algorithms for NER, recommendation systems, and question answering, and exploring generative AI applications. | Post-trainAgent | 9 |
| 2026 Fall Applied Science Internship - Natural Language Processing and Speech Technologies - United States, PhD Student Science Recruiting PhD internship focused on research in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Speech Technologies, including large language models (LLMs) and reinforcement learning with human feedback (RLHF). The role involves developing and implementing novel algorithms on production-scale data to advance the state-of-the-art. | Post-trainPretrain | 9 |
| Principal PMT-ES - AI/ML Training, Annapurna Labs Principal Technical Product Manager to define and drive product strategy for training software on AWS Trainium, including distributed training libraries, post-training workflows (RLHF, DPO, fine-tuning), reinforcement learning frameworks, and training performance optimization. The role focuses on enabling researchers and operators to train frontier models at scale. | DataPost-train | 9 |
| Postdoctoral Scientist, Amazon Robotics Research and AI Development Postdoctoral Scientist role focused on research in multi-agent path planning, dynamic optimal transport, and explainable AI for foundation models applied to a large fleet of mobile robots. The role involves developing novel techniques, publishing in top-tier venues, and potentially extending research into a second year. | AgentPost-train | 9 |
| Principal Applied Scientist , Personalized Autonomy and Proactive Intelligence (PAPI) This role focuses on leading research and development for next-generation proactive and autonomous agentic experiences within Alexa AI. The Principal Applied Scientist will guide a team in leveraging state-of-the-art ML, NLP, LLM training, and agentic AI systems to advance autonomous intelligence and proactive user assistance. Key responsibilities include identifying research directions, developing novel agent solutions, translating research into production, and influencing partner teams to launch AI-powered autonomous agents. The role aims to transform user interaction with Alexa by enabling proactive anticipation of needs, autonomous task execution, intelligent reasoning, continuous learning, and seamless coordination across domains. | 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 |
| Applied Scientist II, Alexa Sensitive Content Intelligence (ASCI) This role focuses on building AI safety systems for conversational AI, specifically for Alexa. It involves pioneering solutions in Responsible AI, training models for safety standards, designing automated testing for vulnerabilities, creating intelligent evaluation systems, building models to understand human values, and crafting feedback mechanisms. A key aspect is building AI agents for real-time detection and fixing of production issues. The role emphasizes frontier research with real-world impact, focusing on training truthful and grounded models, building reward models for human values, and creating automated systems to discover and address issues. Collaboration with scientists, PMs, and engineers is expected to transform ideas into production systems. The role also involves leading certification processes, advancing optimization techniques, building human-like evaluation systems, and mentoring others. | Post-trainEval Gate | 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 |
| Sr. Applied Scientist, Ads AI Core Infrastructure Research and develop novel approaches for agent-data interaction using generative AI and agentic systems to provide instant, strategic advice to advertisers. Focus on agent orchestration, context optimization, code generation, and RAG-based embeddings for real-time data access with minimal latency and token consumption. Balances applied research (60%) with productionization (40%). | Agent | 9 |
