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 |
| 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. 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 |
| 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, 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, 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 |
| 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 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 |
| 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 |
| 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 |
| 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 |
| Sr. Applied Scientist, C360 Senior Applied Scientist role focused on advancing Information Retrieval, NLP, and Large Language Models for e-commerce personalization. The role involves post-training LLMs (instruction tuning, reward modeling, RL, multi-modal alignment), designing large-scale experiments, analyzing model behavior, and developing training recipes to improve capabilities like reasoning and personalization. It also includes owning the scientific roadmap, leading end-to-end systems, driving technical decisions, mentoring, and publishing research. | Post-trainAgent | 8 |
| Senior Applied Scientist, C360 Senior Applied Scientist role focused on improving shopping experiences using LLMs. The role involves post-training activities like instruction tuning, reward modeling, reinforcement learning, and aligning LLMs with embedding modalities. Responsibilities include designing and running large-scale experiments, analyzing model behavior, and developing new training recipes to enhance reasoning and personalization. | Post-trainPretrain | 8 |
| 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 across images, video, and multimedia. The role will innovate in diffusion and flow-based methods, advance visual grounding and 3D estimation, and design multimodal GenAI workflows including agentic pipelines. | Post-trainAgent | 8 |
| Sr. Applied Scientist, Alexa International The Senior Applied Scientist will focus on developing novel algorithms and modeling techniques for multilingual speech generation, text-to-speech synthesis, and speech-to-speech models within the Alexa International team. This role involves driving scientific strategy, influencing partner teams, and delivering solutions that enhance voice experiences across multiple languages, leveraging large-scale computing resources and addressing challenges in low-resource language settings. | Post-trainServe | 8 |
| Sr. Applied Scientist, Alexa International Senior Applied Scientist role focused on developing and advancing multilingual speech models (understanding and generation), text-to-speech synthesis, and speech-to-speech models for Alexa International. The role involves driving scientific strategy, leveraging large-scale computing resources, and optimizing model performance for production deployment in low-resource language settings. | Post-trainServe | 8 |
| Applied Scientist, RL post-training, AWS This role focuses on Reinforcement Learning (RL) post-training of frontier LLMs to improve capabilities like instruction following, reasoning, and tool use, primarily for customer service applications within AWS. The role involves developing innovative solutions, publishing findings, and working with researchers and engineers. | Post-train | 8 |
| Sr Applied Scientist III, Supply Chain Optimization Technologies - SCAIL This role focuses on designing, implementing, and evaluating innovative models and agents using Reinforcement Learning (RL) for supply chain optimization. It involves both advancing theoretical knowledge in ML/AI and applying these insights to real-world business problems, with an emphasis on research and publication. | Post-trainAgent | 8 |
| 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-trainAgent | 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, 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, 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 - 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 |
| 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 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 , 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 |
| 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 |
| 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 |