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 - 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 |
| 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 |
| 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 |
| 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, 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 |
| 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 |
| 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 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 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, 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Applied Scientist, Alexa Connections Applied Scientist role focused on building and evaluating LLMs and multimodal systems for Alexa Connections, involving fine-tuning, post-training, and contributing to research and production delivery. | Post-trainAgent | 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 |
| Principal Applied Scientist, Conversational Assistant Modeling & Learning Principal Applied Scientist to lead science behind Alexa+, Amazon's LLM-powered conversational assistant. Owns technical direction for LLM fine-tuning, alignment, agentic reasoning, and evaluation, impacting hundreds of millions of customers. Defines research directions, designs experiments, ensures translation to production systems, mentors scientists, and represents Amazon in the research community. | Post-trainAgent | 9 |
| Applied Scientist II - AMZ9674020 Applied Scientist II role focused on designing, developing, and deploying data-driven models for ML and NL applications, with a strong emphasis on generative AI, NLP, and large-scale model training and deployment. The role involves researching and implementing novel ML approaches, fine-tuning foundation models, developing custom algorithms for model optimization, and conducting applied research on generative AI architectures and training strategies. Mentoring junior scientists is also a key responsibility. | Post-trainAgent | 9 |
| Applied Scientist III, Alexa International This role focuses on advancing the state of the art with LLMs and multimodal systems for Alexa's international products. The scientist will develop novel algorithms, build evaluation metrics, fine-tune/post-train LLMs using advanced techniques (SFT, DPO, RLHF, RLAIF), and contribute to industry-first research. The role involves end-to-end delivery from research to production, influencing cross-team scientific strategy, and mentoring junior scientists. Key areas include multi-lingual applications, text, speech, and vision domains, with a strong emphasis on LLM evaluation and post-training methodologies. | Post-trainAgent | 9 |
| Senior Applied Scientist, Delivery Foundation Model Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models on large datasets, optimizing for inference at scale, and collaborating with science and engineering teams for production deployments. It requires guiding technical direction, mentoring, and maintaining individual contributions. | Post-trainServe | 9 |
| Member of Technical Staff, Multimodal Reasoning - Applied Science , AGI Autonomy Applied Science role focused on developing foundational capabilities for useful AI agents, leveraging large vision language models (VLMs) with reinforcement learning (RL) and world modeling. Responsibilities include model training, dataset design, and pre- and post-training optimization in an applied research setting. | Post-trainAgent | 9 |
| Applied Scientist, LLM Code Agents, Kiro Science Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems. | Post-trainAgent | 9 |
| Applied Scientist, LLM Code Agents, Kiro Science Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems. | Post-trainAgent | 9 |
| Applied Scientist, Artificial General Intelligence Seeking an Applied Scientist to develop industry-leading technology with LLMs and multimodal systems, focusing on advanced approaches, model-in-the-loop and human-in-the-loop for high-quality data collection and LLM training, and enhancing customer experiences. | Post-trainAgent | 9 |
| Computer Vision Scientist, International Machine Learning, Australia Computer Vision Scientist role focused on developing and evaluating generative AI models for e-commerce media content, leveraging large datasets and cloud resources. The role involves research, implementation of novel ML techniques, and communication with stakeholders. | Post-trainServe | 9 |
| Principal Applied Scientist, Ring AI Principal Applied Scientist role focused on computer vision and multimodal LLMs, involving research, algorithm development, and translating research into practice for consumer products. Requires PhD, 10+ years of ML experience, and expertise in computer vision, VLM, and deep learning. The role involves defining research directions, developing long-term strategies, and mentoring junior scientists. | Post-trainAgent | 9 |
| Senior Applied Scientist, Delivery Foundation Model Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models at scale, optimizing for inference, collaborating with other teams, guiding technical direction, and mentoring junior scientists. It spans the full spectrum from data preparation to model training, evaluation, and inference. | Post-trainServe | 9 |
| Sr. Applied Science Manager, Perfect Order Experience (POE) AI Senior Applied Science Manager leading a team to develop a domain-specific LLM, including pre-training, fine-tuning, and reinforcement learning. The role also involves architecting risk detection systems using multi-modal signals and influencing ranker models for product visibility. The focus is on building and scaling AI solutions for Amazon's Perfect Order Experience. | Post-trainPretrain | 9 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II focused on building and optimizing generative AI training systems, specifically for LLMs and RL post-training pipelines, at Amazon's Stores Foundational AI team. The role involves designing scalable data infrastructure, improving training efficiency and reliability, and translating research algorithms into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II role focused on building and improving generative AI for shopping using LLMs. Responsibilities include designing and implementing stable and efficient training systems for model training and reinforcement learning, developing scalable data infrastructure, and optimizing RL post-training pipelines. The role involves collaborating with scientists and engineers to accelerate innovation and translate research into production-ready systems. | Post-trainData | 8 |
| Software Dev Engineer II, Stores Foundational AI -SFAI Software Development Engineer II at Amazon on the Stores Foundational AI team, focusing on building and optimizing large-scale LLM training infrastructure, including pretraining and RL post-training pipelines, data infrastructure, and observability systems for generative AI in shopping. | Post-trainData | 8 |
| Sr Software Dev Engineer, Stores Foundational AI -SFAI Senior Software Development Engineer focused on building and scaling ML infrastructure for foundational LLMs in Amazon Stores, specifically involving RL post-training pipelines, stability, efficiency, and translating research into production systems. | Post-trainServe | 8 |
| Applied Scientist, Ring AI Applied Scientist role focused on computer vision and machine learning for consumer products (Ring devices). The role involves research, development, evaluation, and deployment of models, with a specific emphasis on Multi-modal LLMs and Vision Language Models. Collaboration with product and engineering teams is key. | Post-trainAgent | 8 |
| 2027 Applied Science Intern (Computer Vision), Amazon International Machine Learning Internship role focused on Computer Vision and Machine Learning research, developing novel solutions and prototypes with potential impact on customer-facing products. Involves collaboration with researchers and publication in top-tier conferences. | Post-train | 8 |
| Applied Scientist, Conversational AI ModEling and Learning Research and develop novel algorithms and modeling techniques for conversational AI using LLMs, focusing on prompt engineering, fine-tuning, and learning from human feedback to improve user experience and enable natural conversations. | Post-trainAgent | 8 |
| Applied Scientist - LLM, Alexa Applied Scientist role focused on training and deploying LLMs for conversational AI systems like Alexa. The role involves end-to-end ownership from research and algorithm development to production deployment and inference infrastructure. | Post-trainServe | 8 |
| Software Development Manager, AWS Neuron SDK - Distributed Training Software Development Manager for AWS Neuron SDK, focusing on distributed training for ML accelerators. The role involves leading a team to design and deploy new products, optimize performance of ML models at scale, and ensure support for key ML functionality. Responsibilities include customer onboarding, maximizing model FLOPS utilization, building tooling, partnering with other teams, and driving technical strategy for frontier model architectures. | Post-trainServe | 8 |
| Applied Scientist II - GenAI/LLM, Translation Services Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, collaborating with cross-functional teams, conducting data analysis, and evaluating state-of-the-art modeling techniques to improve translation accuracy and efficiency. The team has a startup mindset and aims to build scalable solutions from scratch. | Post-train | 8 |
| Applied Science Manager , C360 Manager for a team working on LLM and VLM post-training and alignment for personalized shopping experiences, leveraging customer behavioral data. | Post-trainAgent | 8 |