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 |
|---|---|---|
| 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 |
| 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 |
| Senior PMT ES - Reinforcement Learning, SageMaker AI Senior Product Manager, Technical to define and own the product strategy for reinforcement learning (RL) on Amazon SageMaker AI. The role involves shaping how customers leverage RL for foundation model alignment, customization, and improvement, making RL more accessible for a broad range of customers. | 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 |
| Applied Science Manager, Alexa International Manager for a team of Applied Scientists focused on building and enhancing multilingual speech models (understanding and generation) for Alexa. The role involves leading the team, setting technical direction, driving scientific strategy, and ensuring end-to-end delivery of speech quality improvements from research to production. Key areas include speech-to-speech models, text-to-speech synthesis, multilingual systems, and leveraging large-scale data and computing resources. | 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 |
| Senior Machine Learning Engineer, AWS Generative AI Innovation Center Senior Machine Learning Engineer at AWS Generative AI Innovation Center focused on designing, implementing, and optimizing generative AI solutions for AWS customers. The role involves working with customers to develop bespoke solutions, including fine-tuning and optimizing SLM/LLM models, and addressing complexities in distributed training and low-latency model hosting. | 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 II, Alexa Edge AI Applied Scientist II on the Alexa Edge AI team, focusing on deep learning and speech processing to develop novel ML algorithms for speech and audio. This role involves applied research, model design, training, and optimization for consumer products. | Post-train | 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 the potential for production impact. Collaboration with researchers and publication in top-tier conferences are key aspects. | Post-trainServe | 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 role focused on developing and evaluating LLMs and multimodal systems for Alexa's international products. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs (SFT, DPO, RLHF, RLAIF), setting up experimentation, and contributing to research and production delivery. Requires strong ML, NLU, LLM architecture, and evaluation knowledge, with a focus on international customer nuances and diverse data sources. | 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 from research to production, impacting international customers with digital assistant technology. | 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 from research to production, impacting international customers with digital assistant technology. | Post-trainAgent | 8 |
| Applied Scientist, SSG Science Applied Scientist role focused on optimizing Generative AI models for edge devices, involving quantization, pruning, distillation, and fine-tuning. The role also requires understanding and inventing optimization techniques for custom ML hardware and collaborating with hardware architects and compiler engineers. The goal is to develop production-ready edge models and publish research findings. | Post-trainServe | 8 |
| Applied Scientist, Customer Behavior Analytics Scientist role focused on designing and developing machine learning solutions for customer behavior analytics, utilizing deep learning, LLMs, recommendation systems, and reinforcement learning. Key responsibilities include fine-tuning generative models, developing recommendation and decision models, building behavioral representations, applying post-training optimization, and creating evaluation frameworks. The role emphasizes measurable business impact and customer satisfaction. | 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, Customer Behavior Analytics This role focuses on designing and developing machine learning solutions for customer behavior analytics at Amazon. Key responsibilities include fine-tuning language and generative models, developing recommendation and decision models, building temporal representations of customer behavior, and applying post-training optimization techniques. The role also involves developing evaluation frameworks and working with business and engineering teams to drive personalized customer experiences and business impact. | Post-trainAgent | 8 |
| Applied Scientist, GenAI Evaluation Media Applied Scientist role focused on Generative AI for visual media, specifically in 3D Generative AI and Inverse Rendering. The role involves building scalable CVML models, automating their application, and designing/building pipelines to train and deploy ML models. Expertise in areas like Neural Fields, NeRFs, GANs, Diffusion Models, and differentiable rendering is required. The role bridges computer graphics, computer vision, and deep learning to improve customer experience with product imagery and videos. | Post-trainServe | 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 |
| Machine Learning Engineer , Data & Machine Learning (DML) Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage. | Post-trainAgent | 8 |
| Principal Applied Scientist, PXT This role leads the science strategy and technical vision for an intelligence layer using GenAI and predictive modeling, focusing on heterogeneous signals to power talent applications at Amazon scale. The Principal Applied Scientist will guide a team, conduct hands-on research in areas like foundation models and multi-modal LLMs, design novel ML architectures, and mentor scientists while contributing technically to complex problems. | Post-trainAgent | 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, conducting data analysis, and collaborating with cross-functional teams to improve translation accuracy and efficiency for millions of customers worldwide. | Post-train | 8 |
| Principal Applied Scientist, Data Center Design Engineering - BIM & AI Technologies Principal Applied Scientist role focused on AI-powered design automation for AWS data centers. The role involves defining research roadmaps, developing and deploying ML models (including fine-tuning foundation models, GNNs, NLP, RL, CV) for BIM and AECO applications, and publishing research findings. It requires a blend of theoretical ML knowledge and practical application in a domain with high trust requirements. | Post-trainAgent | 8 |
| Machine Learning Engineer , Data & Machine Learning (DML) Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage. | 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 |
| 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 |
| 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, Amazon Compliance and Safety Services Research Scientist role focused on applying and extending state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning, and large language models to improve product compliance and safety at Amazon. The role involves researching and evaluating algorithms, designing new algorithms for business impact (e.g., synthetic data generation, active learning, grounding LLMs), and collaborating with engineering and product teams to implement ML solutions across the product catalog. The team specializes in image and document understanding for compliance capabilities, with a focus on publishing research. | Post-train | 8 |
| Applied Scientist, AGI Customization Services Applied Scientist role focused on developing and customizing large language models for enterprise use cases, involving techniques like supervised fine-tuning, reinforcement learning, and knowledge distillation. The role requires building enterprise-ready tooling, optimizing models, and contributing to responsible AI toolkits. | Post-trainData | 8 |
| Senior Applied Scientist, HST Health Evaluation Senior Applied Scientist role focused on developing and deploying AI/ML solutions for healthcare, specifically involving LLMs and VLMs, with an emphasis on model optimization and fine-tuning for production. | Post-trainServe | 8 |
| Data Scientist - II, Alexa Sensitive Content Intelligence The Data Scientist-II role on the Alexa Sensitive Content Intelligence (ASCI) team focuses on building AI safety systems for Alexa's next-generation AI-powered virtual assistant. This involves developing responsible AI (RAI) solutions to ensure LLMs provide safe and trustworthy responses, understanding nuanced human values, and maintaining customer trust. The role requires applying state-of-the-art Generative AI techniques to analyze data, run experiments, and optimize data for sensitive content detection and mitigation, working with LLMs and multimodal systems. | Post-trainData | 8 |
| Data Scientist - II, Alexa Sensitive Content Intelligence The Data Scientist-II role on the Alexa Sensitive Content Intelligence (ASCI) team focuses on building AI safety systems for Alexa's next-generation AI-powered virtual assistant. This involves developing responsible AI (RAI) solutions to ensure LLMs provide safe and trustworthy responses, understanding nuanced human values, and maintaining customer trust. The role requires applying state-of-the-art Generative AI techniques to analyze data, run experiments, and optimize data for sensitive content detection and mitigation, working with LLMs and multimodal systems. | Post-trainData | 8 |
| Applied Scientist II, HST Health Evaluation Applied Scientist II role focused on developing and optimizing state-of-the-art AI/ML solutions for healthcare, specifically LLMs and VLMs, with a focus on production deployment and model distillation. | Post-trainServe | 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 |
| 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 at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research. | Post-trainData | 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 at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research. | Post-trainData | 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 |
| Senior Software Development Engineer , Stores Foundational AI - Rufus Senior Software Development Engineer focused on building and scaling foundational LLMs for Amazon Stores. The role involves architecting and building ML infrastructure for LLM training and post-training workflows (fine-tuning, RL, continuous learning), transforming customer interactions into training signals, optimizing RL systems, and partnering with scientists to productionize frontier techniques like RLHF and agentic workflows. Emphasis on end-to-end system ownership, including design, implementation, deployment, and observability, with a focus on low-level optimization like CUDA kernels and ML platforms. | Post-trainServe | 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 |
| Sr. Machine Learning Engineer, WWPS ProServe Data and Machine Learning Senior Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. The role involves working with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and implement AI/ML solutions at scale. It also includes designing and running experiments, researching new algorithms, and optimizing for business impact. The role requires expertise in machine learning, generative AI, and best practices, with a focus on customer success and AI transformation. | Post-trainAgent | 8 |
| Sr. Data Scientist- Computer Vision, Data & Machine Learning (DML) Develop computer vision models on overhead imagery for a government customer, owning the entire ML development lifecycle from data exploration and feature engineering to model training, evaluation, and delivery. This role operates on classified networks and requires a Top Secret security clearance. | Post-trainData | 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 |