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 724 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Principal, Applied Scientist, AWS Applied AI Solutions This role focuses on leading technical innovation in visual reasoning foundation models, specifically building a next-generation visual reasoning engine powered by frontier Large Video Models (LVMs). The goal is to create a system that rivals human understanding of the physical world, capable of interpreting natural language, navigating environments, and executing complex tasks. It sits at the intersection of LVMs, LLMs, and Agentic AI, requiring end-to-end ownership from research to production deployment, with a focus on advancing state-of-the-art and solving real-world business problems. | AgentPost-train | 10 |
| Applied Scientist - LLM, Alexa Conversational Modelling Intelligence Applied Scientist II in Alexa Conversational Modelling Intelligence team focused on LLM post-training (SFT, RLHF, preference optimization) for Alexa+. Drives model development from data curation through training, evaluation, and deployment. Builds evaluation frameworks, diagnoses defects, and iterates on recipes. Collaborates with scientists and engineers, contributes to tooling, and publishes research. Aims to improve customer experience for millions. |
| Post-trainServe |
| 9 |
| 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 |
| Postdoctoral Scholar - SAF Lab, Compass Research role focused on developing and validating safe autonomy for highly dynamic robots, integrating control barrier functions (CBFs) with perception and learning, and deploying methods on physical robotic hardware. The work involves pushing the frontiers of safety theory, developing simulation and evaluation pipelines, and enabling robots to operate safely around humans. | ShipData | 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, Alexa AI Senior Applied Scientist role focused on defining and driving the science roadmap for conversational AI systems using LLMs, impacting millions of customers. Responsibilities include designing LLM fine-tuning, alignment, and agentic architectures, owning end-to-end delivery from research to production, developing evaluation frameworks, and collaborating with cross-functional teams. The role involves publishing at top-tier conferences and generating intellectual property. | AgentPost-train | 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 |
| Principal Applied Scientist Perception, Compass Seeking a Principal Applied Scientist to lead safety-critical perception for robots, developing novel real-time predictive models of dynamic environments and human motion. This role involves architecting generalizable perception pipelines across sensor modalities, investigating foundation models, and quantifying perception uncertainty to ensure safe robot autonomy. | AgentData | 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 |
| Applied Scientist II Applied Scientist II at Amazon focusing on Speech and Language technology, including ASR, NLU, MT, TTS, Dialog Management, and CV. The role involves developing novel algorithms and modeling techniques to advance the state-of-the-art and impact millions of customers through large-scale systems. | Post-train | 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 |
| Applied Scientist, Conversational Assistant Modeling and Learning Applied Scientist role at Amazon focusing on building Alexa+, an LLM-powered conversational assistant. Responsibilities include LLM fine-tuning, alignment, agentic reasoning, and evaluation pipelines. The role involves designing and implementing end-to-end systems, translating research into production, and publishing results. It operates at massive scale across multiple languages and device types. | AgentPost-train | 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 |
| Principal Applied Scientist, ML Codesign This role is for a Principal Applied Scientist focused on the joint optimization of model compression and silicon architecture for AI inference accelerators. The scientist will define the hardware-aware compression roadmap, own the optimization of compression algorithms with hardware, and influence silicon architecture decisions. The goal is to ship advanced compression techniques and large models on next-generation accelerators, bridging the gap between model accuracy and hardware efficiency. | ServePost-train | 9 |
| Member of Technical Staff, FAR (Frontier AI & Robotics) Research role focused on developing foundation models for robotics, involving multi-modal understanding, sim2real transfer, and efficient inference, with a goal of large-scale deployment. | PretrainServe | 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 |
| Applied Scientist, Selling Partner Support Engagement Research scientist role focused on building and improving AI agents for customer support, involving RL-based systems, preference learning, reward modeling, and policy optimization. The role emphasizes end-to-end ownership from research to production deployment, collaboration with engineering teams, and publishing research. It operates within an enterprise AI domain focused on scaling AI solutions for customer 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, Trust CX Innovations&AI Policy Senior Applied Scientist role focused on Generative AI, LLMs, and multimodal models for Alexa+, emphasizing AI trust, privacy, safety, and alignment. The role involves leading research, developing optimization techniques, pioneering responsible AI methods, and collaborating with product and engineering teams to deliver production-ready AI solutions. | Post-trainAgent | 9 |
| Applied Scientist, Alexa International Tech Applied Scientist role focused on developing Generative AI (GenAI) technology with LLMs and multimodal systems, specifically for speech generation to make Alexa more natural, expressive, and culturally relevant across multiple languages. The role involves research, algorithm development, experimentation, publishing findings, and collaborating with engineering teams to productionize models. | Post-trainServe | 9 |
| Senior Applied Scientist, Amazon AWS Agentic AI, AWS AI Fundamental Research This role focuses on leading the design and development of agentic evaluation frameworks and training evaluation/critic models to assess the quality and effectiveness of AI agents. The scientist will define methodologies, create benchmarks, build automated systems, and conduct research to advance agent and evaluation science. The role involves end-to-end ownership from research to production deployment, collaborating with engineering to deliver these capabilities as managed AWS services. It also includes mentoring junior scientists and contributing to the research community. | Eval GatePost-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 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 |
| 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 |
| Human-Robot Interaction Applied Scientist , Fauna Seeking an HRI Applied Scientist to develop cutting-edge interactions for robots, focusing on verbal/non-verbal systems, social dynamics, memory, and long-term relationships. The role involves developing interactive systems using LLMs, multimodal inputs/outputs, and RLHF, designing conversational systems, integrating sensor streams, and developing memory/personalization systems. The scientist will stay updated on HRI/ML/AI/HCI advancements, lead technical projects, mentor junior staff, and bridge research with engineering. | AgentPost-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 Scientist II, Amazon AWS Agentic AI, AWS AI Fundamental Research Research scientist role focused on building industry-leading generative AI and foundational models, with a specific emphasis on Agentic AI, impacting millions of customers through speech, vision, and language technologies. The role involves developing novel algorithms and modeling techniques, working with large-scale data and computing resources. | AgentPost-train | 9 |
| Applied Scientist, Prime Video - Generative AI Applied Scientist role focused on Generative AI for Prime Video, involving research and development of generative models for synthesis (images, video, multimedia), advancing diffusion and flow-based methods, and designing multimodal GenAI workflows including agentic pipelines. The role aims to deliver production-ready systems at Amazon scale. | Post-trainAgent | 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, Alexa Edge AI Applied Scientist role focused on designing, developing, and deploying multimodal ML models (CV, audio, speech) for edge and cloud deployment. The role involves full ML lifecycle ownership, research, and contributing to publications. It's a founding member position in a new Bangalore site, requiring collaboration with engineers and scientists to productionize algorithms. | Post-trainServe | 9 |
| Applied Scientist, Alexa Edge AI Seeking an Applied Scientist to design, develop, and deploy state-of-the-art ML models for computer vision, audio, and multimodal understanding for edge and cloud deployment. This role involves full ML lifecycle ownership, research, and contributing to publications, with an opportunity to be a founding member of a new site. | Post-trainServe | 9 |
| Applied Scientist, Alexa Edge AI This role focuses on researching and developing next-generation machine learning models for computer vision, audio processing, and multimodal semantic understanding, with a strong emphasis on defining the science roadmap, delivering scalable solutions, and publishing research. The role involves technical leadership, end-to-end ownership of ML programs, and mentorship. | Post-trainServe | 9 |
| Applied Scientist, Amazon Robotics Applied Scientist role focused on developing and training foundation models for robotics, integrating multi-modal learning, imitation learning, and reinforcement learning. The role involves model development, data management, experimentation, and research to enhance robotic perception and skill acquisition. | Post-trainAgent | 9 |
| 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 |
| 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 |
| 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 |
