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 |
|---|---|---|
| Applied Scientist I Applied Scientist role focused on developing novel algorithms and modeling techniques for speech and language technologies (ASR, NLU, TTS, Dialog Management) impacting millions of customers. Requires ML background and programming experience, with preferred qualifications in AI/ML technologies, large-scale systems, and publications in top-tier conferences. | Post-train | 8 |
| Applied Scientist II, Alexa International Team Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology. | Post-train |
| 8 |
| Senior Applied Scientist, Neuro-Symbolic AI Labs Research scientist role focused on developing neuro-symbolic AI systems that integrate proof assistants for enhanced learning and reasoning, applied across various Amazon domains. The role involves defining and implementing new applications, delivering scientific artifacts, and working in an agile environment. | Post-train | 8 |
| Applied Scientist, Amazon Compliance and Safety Services Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety within Amazon. The role involves evaluating existing algorithms, designing new ones, generating synthetic data, and potentially using active learning and grounding LLMs for business use cases. Collaboration with engineers and product managers is key, with an emphasis on publishing research. | Post-trainData | 8 |
| Applied Scientist II, RBS Tech The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research. | AgentPost-train | 8 |
| Applied Scientist II, RBS Tech The Applied Scientist II, RBS Tech role focuses on foundational ML research and developing scalable ML solutions for customer experience (CX) and Selling Partner experience (SPX). The role involves designing and deploying GenAI, NLP, and Computer Vision solutions, developing novel LLM, deep learning, and statistical techniques for task automation, text and image processing, pattern recognition, and anomaly detection. It also includes defining research strategies, partnering with business and engineering teams, and potentially filing patents or publishing research. | AgentPost-train | 8 |
| Applied Scientist II, Prime Video Personalization and Discovery Science Applied Scientist II at Amazon Prime Video focusing on personalization and discovery. The role involves developing foundation models for content understanding (video, text) and customer behavior prediction using deep learning and multimodal techniques. Responsibilities include building time sequence models, end-to-end solution implementation with engineers and product managers, designing and conducting A/B experiments, and publishing research findings. The team works on recommendation science for Prime Video surfaces and devices, aiming to solve cold-start problems and discover niche customer interests. | Post-trainAgent | 8 |
| Sr. Applied Scientist, Prime Video - Personalization and Discovery Science Senior Applied Scientist role focused on developing and launching foundation models for content understanding and customer behavior prediction within Prime Video. The role involves hands-on machine learning, research leadership, and end-to-end ownership of solutions, with an emphasis on publishing research findings. | Post-trainAgent | 8 |
| Applied Scientist II, Amazon Connect Research and develop generative AI technology for Amazon Connect, focusing on LLM Agents and their evaluation/optimization to disrupt customer service experiences. The role involves building ML models from conception to deployment, prototyping, and iterating on state-of-the-art Agentic AI systems. | AgentPost-train | 8 |
| Applied Scientist II The role focuses on developing and applying cutting-edge simulation methodologies for advanced robotics systems, including physics-based simulation, sim-to-real transfer, and machine learning. The goal is to enable rapid development, testing, and validation of robotic systems in complex environments. The role involves fundamental research and real-world development, translating research into scalable simulation capabilities that impact robot design and building. | DataAgent | 8 |
| Applied Scientist II, Reinforcement Learning Applied Scientist II role focused on developing advanced robotics systems using AI, deep learning, and reinforcement learning for automation at Amazon's scale. The role involves designing and implementing control methods for balance, locomotion, and manipulation, with a focus on bridging theoretical advancements and practical implementation in robotics. | Ship | 8 |
| Applied Scientist, AGI , AGI Information This role focuses on advancing knowledge graphs for the LLM era, specifically for LLM grounding and construction pipelines. It involves web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory over graphs. The primary responsibility is entity resolution for conflating facts from multiple sources into a single graph entity, requiring scalable, generic, and streaming data solutions. The role also touches upon agent memory, suggesting a secondary stage involvement. | DataAgent | 8 |
| Applied Scientist, Traffic Quality Applied Scientist II role focused on detecting sophisticated invalid traffic (IVT) in advertising using deep learning, self-supervised techniques, representation learning, and advanced clustering. The role involves defining research problems, inventing ML approaches, designing and deploying production-quality ML components, and working with massive datasets. It also requires producing research reports and contributing to the scientific community through publications. | Agent | 8 |
| Applied Scientist II, Alexa International Team Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery of solutions impacting international customers. | Post-trainAgent | 8 |
| 2026 Fall Applied Science Internship - Information & Knowledge Management (Machine Learning) - United States, PhD Student Science Recruiting This internship focuses on developing systems and frameworks for machine learning asset lifecycle management, leveraging NLP and information retrieval. The role involves research into ML operations and knowledge engineering to enhance Amazon's ML capabilities. | DataPost-train | 8 |
| 2026 Fall Applied Science Internship - Reinforcement Learning & Optimization (Machine Learning) - United States, PhD Student Science Recruiting This internship focuses on research in Reinforcement Learning and Optimization within Machine Learning, developing and implementing novel algorithms for complex real-world challenges. The role involves working with large-scale data and applying cutting-edge ML techniques. | Post-train | 8 |
| Sr. Applied Scientist, Prime Video - Personalization and Discovery Science This role focuses on developing and launching end-to-end AI solutions for Prime Video's recommendation and personalization systems. It involves deep learning, GenAI, reinforcement learning, and optimization methods, with a strong emphasis on experimental design (A/B testing) and research publication. The scientist will work closely with engineers and product managers to bring these solutions to millions of customers. | Ship | 8 |
| Senior Applied Scientist, Personalization, Personalization Strategic Initiatives Science Senior Applied Scientist role focused on research, design, and development of new AI technologies for personalization, including recommendation systems and large language models. The role involves inventing, experimenting with, and launching new features, products, and systems that impact millions of customers. | ShipPost-train | 8 |
| Applied Scientist, Personalization, Personalization Strategic Initiatives Science Research Scientist role focused on developing and launching new AI technologies for personalization, leveraging large datasets and computational resources to build large-scale machine learning solutions for customer recommendations. The role involves inventing, experimenting with, and launching new features, products, and systems, with a strong emphasis on research publications. | ShipPost-train | 8 |
| Applied Scientist Intern, 2026 Shenzhen This internship focuses on bridging cutting-edge AI research with practical application and communication. The intern will translate complex AI concepts into understandable content for business stakeholders and the wider community, document AI capabilities, develop internal AI literacy programs, and contribute to applied research projects in NLP, Computer Vision, or Multimodal AI. The role requires a strong foundation in ML/DL, Python, and ML frameworks, with a passion for science communication and a curious, open mindset. | Post-trainAgent | 8 |
| Data Scientist, Demand Forecasting Research Scientist role focused on building and deploying large-scale foundation models for demand forecasting at Amazon. The role involves designing experiments, developing deep learning and statistical models, and analyzing large datasets to improve forecasting accuracy and downstream business impact. Emphasis on research rigor, production deployment, and scientific contribution. | Post-train | 8 |
| Applied Scientist, Last Mile Delivery Automation This role focuses on developing AI and ML solutions for last mile delivery automation, combining expertise in machine learning, computer vision, and robotics to solve complex challenges in perception, navigation, and path planning. The scientist will research, design, and implement algorithms, transforming research concepts into production-ready solutions for autonomous systems. | ShipAgent | 8 |
| Applied Scientist II, Alexa AI Applied Scientist II at Amazon Alexa AI focused on prototyping, optimizing, and deploying ML algorithms in Generative AI. Responsibilities include research, building PoCs, collaborating with teams, technical communication, documentation, and publishing research. | Post-train | 8 |
| Applied Scientist, Sales AI This role focuses on building AI/ML solutions for the Ad Sales business, specifically creating customer-facing recommendations and enhancing end-to-end workflows with Generative AI. The scientist will leverage quantitative modeling techniques like Sequential Recommender Systems, Deep Learning, and Reinforcement Learning, and use NLP and Generative AI for explainability. The role involves research, model development, A/B testing, and collaboration with engineering and product teams to deliver production-ready solutions. | AgentPost-train | 8 |
| Applied Scientist , Amazon Applied Scientist role at Amazon focusing on improving shopping experiences using LLMs. The role involves post-training of LLMs, including instruction tuning, reward modeling, and reinforcement learning. Responsibilities include designing and running large-scale experiments, analyzing model behavior, and developing new training recipes to enhance capabilities like reasoning and user experience. Requires a PhD or Master's with significant experience, practical LLM experience, and a strong publication record. | Post-trainPretrain | 8 |
| Applied Scientist, Console Science The Applied Scientist will work on building industry-leading Conversational AI Systems, focusing on Natural Language Understanding, Dialog Systems, Generative AI with LLMs, and Applied Machine Learning. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. The scientist will gain hands-on experience with Amazon's text, structured data, and large-scale computing resources. | Post-trainAgent | 8 |
| Sr Manager Research Science, Last Mile Science and Analytics This role focuses on applying AI and machine learning to optimize Amazon's last-mile delivery network. Responsibilities include developing sophisticated ML models for logistics, forecasting, and resource allocation, architecting AI-powered systems, implementing deep learning for image recognition, and developing reinforcement learning for adaptive scheduling. The role also involves designing AI agents for autonomous decision-making and creating models for customer behavior analysis. A strong emphasis is placed on research, publishing findings, and leveraging big data and cloud platforms. | AgentData | 8 |
| Applied Scientist II, Foundation Model, Industrial Robotics Group Applied Scientist II role focused on developing foundation models for industrial robotics, integrating multi-modal learning, skill acquisition, perception, and environmental understanding. The role involves leveraging, adapting, and optimizing state-of-the-art models, conducting rigorous experimentation, building evaluation benchmarks, and contributing to data and training workflows. It requires strong programming skills in Python and experience in deep learning areas like computer vision, multimodal models, or RL for robotics. | Post-trainAgent | 8 |
| Applied Scientist, Last Mile Delivery Automation Applied Scientist role focused on developing AI/ML solutions for Last Mile Delivery Automation, combining expertise in machine learning, computer vision, and robotics for perception, navigation, and path planning. The role involves transforming research into production-ready solutions and collaborating with engineering teams. | AgentServe | 8 |
| Applied Scientist II, Business Data Technologies This role focuses on designing and deploying GenAI, NLP, and Computer Vision solutions for Amazon's retail business services, aiming to enhance customer experience and operational efficiency. The scientist will develop novel ML techniques for task automation, text and image processing, and anomaly detection, with a strong emphasis on LLM Agents and multi-modal understanding. | AgentPost-train | 8 |
| Applied Scientist II, Amazon Smart Vehicles The Amazon Smart Vehicles (ASV) science team is seeking an Applied Scientist with expertise in advanced LLM technologies to create personalized services for drivers and passengers, enhancing their experience on the road. The role involves innovating in AI research, developing novel algorithms, and applying theoretical models in an applied environment, with direct application to Amazon products. The scientist will leverage Amazon's data and computing resources to advance generative AI and work closely with other teams to drive impact. | Agent | 8 |
| Amazon Industrial Robotics - Applied Scientist II Intern / Co-op - 2026, Amazon Industrial Robotics This role focuses on developing next-generation advanced robotics systems by combining AI, control systems, and mechanical design for automation at Amazon's scale. The intern will contribute to research bridging theoretical advancements and practical implementation in robotics, focusing on areas like dexterous manipulation, locomotion, and human-robot interaction, leveraging deep learning and LLMs. | Ship | 8 |
| Senior Applied Scientist, LLM Code Agents, Kiro Science Senior Applied Scientist role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a strong emphasis on research, publication, and deploying these models into production systems for developers. | Post-trainAgent | 8 |
| Research Scientist, SSG Science Research Scientist role focused on developing and optimizing Generative AI models for edge devices, involving model compression techniques, custom ML hardware, and theoretical understanding of deep learning and information theory. The role involves co-authoring research papers and collaborating with cross-functional teams. | ServePost-train | 8 |
| Robotics - Applied Scientist II Intern / Co-op - 2026 (Robotics, Manipulation, Perception, Motion Planning, Autonomous Mobile Robots, Computer Vision, Machine Learning, Controls, and more) This role is for a PhD student intern/co-op focused on robotics research, specifically in areas like manipulation, perception, motion planning, and autonomous mobile robots. The role involves applying machine learning, computer vision, and potentially LLMs to solve real-world robotics problems, with a focus on developing research prototypes and seeing them through from concept to working prototype. The work touches on data collection/preparation for training and research. | AgentData | 8 |
| Sr. Applied Science , AWS Agentic AI This role focuses on building next-generation models for intelligent automation within AWS Agentic AI. The scientist will develop innovative solutions for complex problems, focusing on areas like autonomous agents, API orchestration, planning, large multimodal models (especially vision-language), reinforcement learning, and sequential decision making. The role involves partnering with technology and business teams, utilizing extensive data and computational resources, and collaborating with engineers. There's an expectation to publish findings at peer-reviewed conferences. | Agent | 8 |
| Sr. Manager Applied Science, MLA Lead a team of scientists to research and prototype Machine Learning applications, focusing on Agentic AI and LLM solutions for seller experience, trust, and safety. The role involves designing and implementing large-scale, end-to-end business solutions and influencing technical strategy. | Agent | 8 |
| Sr. Applied Scientist, Global Hiring Science This role focuses on applying machine learning and AI to reinvent the hiring process at Amazon, aiming for scale, sophistication, and accuracy in talent acquisition. The scientist will work on state-of-the-art research, advanced software tools, new AI systems, and ML algorithms, leveraging Amazon's tech stack to deliver innovative solutions for hiring. | Ship | 7 |
| Applied Scientist, SCOT Forecasting and Labs - CIV Team Applied Scientist role focused on developing and prototyping new statistical, causal, and machine learning techniques for inventory availability and delivery speed estimations in Amazon's retail supply chain. The role involves collaborating with software teams for production implementation and analyzing business metrics. | Post-train | 7 |
| Applied Scientist, Silicon and Systems Group Edge AI Research Scientist role focused on developing novel evaluation methods for multimodal language models and agents for consumer devices. This involves creating and validating automated evaluation techniques, analyzing datasets to understand model gaps, and collaborating with training teams. The role emphasizes hardware-software integration for efficient model training and deployment on edge devices. | Eval GatePost-train | 7 |
| Applied Scientist, Pricing Science Applied Scientist role focused on developing and launching customer-obsessed improvements to pricing algorithms for billions of Amazon products, leveraging large-scale multi-modal datasets and predictive modeling, causal evaluation, and optimization techniques. | Ship | 7 |
| Research Scientist, Safety-Critical Control, Robotics, SAF Lab Research Scientist focused on developing Control Barrier Function (CBF) theory and algorithms for safety-critical control in robotics. The role involves creating algorithms with formal safety guarantees, integrating them with learned control policies, and deploying them on next-generation robots. Key responsibilities include developing novel CBF algorithms, framing safety filtering within layered architectures involving learning-based components, designing multi-layer CBF filters, and formalizing the interplay between models and system dynamics. The role also requires implementing real-time optimization solvers, validating algorithms through simulation and hardware experiments, and contributing to theoretical foundations through publications. Collaboration with various teams and product leaders is essential for setting a science roadmap. | ShipServe | 7 |
| Applied Scientist, Amazon Redshift Research scientist to build deep learning models for predicting query resource consumption in Amazon Redshift, covering the full ML lifecycle from data analysis to production deployment and publication. | Post-trainServe | 7 |
| Applied Scientist, Prime Video - Content Localization, Understanding & Enrichment This role focuses on research and development of speech and audio generation technology, including end-to-end speech-to-speech architecture and audio processing solutions. The scientist will define research roadmaps, publish findings, and develop deep learning algorithms, with a focus on computer vision algorithms. The role involves building models for business applications and potentially mentoring/hiring other scientists. | Post-trainData | 7 |
| Applied Scientist II, Customer360 Research and develop new AI technologies for personalization using recommendations, information retrieval, and large language models. Build large-scale ML solutions for customer experiences. | Ship | 7 |
| Research Scientist, Operational Efficiency, AET Planning and Analytics Science Research Scientist role focused on building novel solutions for workforce optimization using operations research, causal inference, machine learning, and generative AI. The role involves designing simulation and optimization models for scheduling, hiring, and task assignment, developing causal inference frameworks, and collaborating with senior leaders to influence strategic decisions. The work directly impacts operational efficiency and employee experience at Amazon's global scale. | Agent | 7 |
| Applied SCI III - AMZ007408, AWS Science of Security This role focuses on the design, development, evaluation, and deployment of formal reasoning systems for security, privacy, and data protection in cloud environments. It involves applying formal verification and automated theorem proving, leading research in AI security, evaluating threats to Generative AI, and developing safeguards. The role also requires building and implementing scalable software solutions for AI systems, data privacy, security, or automated reasoning, with experience in compiler development, static program analysis, or formal/symbolic AI systems. | Eval GateAgent | 7 |
| Applied Scientist, Amazon Shopping Personalization Research Scientist role focused on developing and launching new AI technologies for Amazon's Personalization organization, specifically in recommendation systems and large language models, impacting millions of customers. | ShipAgent | 7 |
| Applied Scientist, Sales AI This role focuses on applying AI/ML, particularly Generative AI, to optimize the Ad Sales business by creating actionable insights and recommendations for account teams and improving their end-to-end workflows. The scientist will build and refine models using statistical methods, deep learning, and reinforcement learning, and leverage NLP and Generative AI for explainability. The role involves research, A/B testing, collaboration with engineering and product teams, and translating complex findings into business recommendations. | Agent | 7 |
| Applied Scientist, AWS Automated Reasoning This role focuses on automated reasoning within AWS, requiring a Ph.D. and experience in areas like SAT, SMT, and program analysis to solve complex problems and drive innovation in AWS services. | Agent | 7 |
