Currently tracking 1110 active AI roles, down 16% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $194k).
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.
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 |
|---|---|---|
| Member of Technical Staff, Artificial General Intelligence The AGI team is looking for a Member of Technical Staff to build industry-leading Generative AI (GenAI) technology with LLMs and multimodal systems. This role involves leading foundational model development in an applied research capacity, including model training, dataset design, and pre- and post-training optimization, leveraging Amazon's resources to advance LLMs and impact customer-facing products and services. | PretrainPost-train | 9 |
| Member of Technical Staff, FAR (Frontier AI & Robotics) Research role focused on developing foundation models for robotics, involving multi-modal understanding, sim2real transfer, and efficient inference, with a goal of large-scale deployment. |
| PretrainServe |
| 9 |
| Software Engineer I - AI/ML, AWS Neuron Distributed Training Software Engineer role focused on developing, enabling, and optimizing large-scale ML model training (pre-training and post-training of LLMs, multimodal, and RL workloads) on AWS Trainium accelerators. This involves working with distributed training frameworks, mixed-precision techniques, and performance tuning on specific hardware. | PretrainPost-train | 9 |
| Applied Scientist, Demand Forecasting Research scientist role focused on designing and building large-scale foundation models for time series demand forecasting. The role involves developing novel architectures, training strategies, and data generation techniques, with a strong emphasis on both scientific research (publications) and production deployment impacting millions of dollars in automated decisions. Experience with transfer learning, zero-shot forecasting, and synthetic data generation is key. | PretrainServe | 9 |
| Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training Senior Software Engineer role focused on developing, enabling, and optimizing large-scale ML model training (pre-training and post-training) on AWS Trainium accelerators. This involves working with distributed training frameworks, mixed-precision techniques, and performance tuning across various model families including LLMs, multimodal models, and RL workloads. | PretrainPost-train | 9 |
| Sr. Principal Scientist, Amazon Health Science & Analytics Senior AI/ML researcher to define ML strategy for a healthcare foundation model and inference system, focusing on frontier models, proprietary domain models, and monetizable features under regulatory constraints. Requires expertise in training/adapting large models, distributed training, RLHF/DPO, retrieval, evaluation, and ML systems engineering, with experience in high-stakes/regulated domains. | PretrainServe | 9 |
| Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training Senior Software Engineer role focused on developing, enabling, and performance tuning distributed training solutions for large-scale ML models (LLMs, Stable Diffusion, ViT) on AWS Neuron accelerators using PyTorch. The role involves building distributed training support into PyTorch, the Neuron compiler, and runtime stacks, with a focus on strategies like FSDP, PP, and Context parallel. Experience with post-training strategies is a plus. | PretrainPost-train | 9 |
| Senior Applied Scientist, Delivery Foundation Model Senior Applied Scientist role focused on developing and implementing novel foundation models for logistics, combining multimodal data (image, video, geospatial) and large-scale training/inference. The role involves guiding technical direction, mentoring, and collaborating across science and engineering teams to deploy these models for various Amazon delivery use cases. | PretrainServe | 9 |
| Principal Applied Scientist, Delivery Foundation Model Principal Applied Scientist role focused on developing and implementing novel foundation models for Amazon's delivery logistics. The role involves designing deep learning architectures, training models on vast datasets, and ensuring production-level performance for multimodal data (image, video, geospatial). It emphasizes scientific leadership, collaboration, and mentoring, with a strong focus on both research and engineering aspects of foundation model development and deployment at scale. | PretrainServe | 9 |
| Principal Applied Scientist, FAR (Frontier AI & Robotics) Lead the development of breakthrough foundation models for robotics, focusing on perception, manipulation, and interaction with the world. This role involves hands-on research, algorithm design, and scaling models for real-world deployment at Amazon scale, with a focus on multi-modal and efficient architectures. | PretrainServe | 9 |
| Applied Scientist, Delivery Foundation Model Applied Scientist role focused on developing and implementing novel foundation models for logistics, involving multimodal data, training at scale, and inference. The role spans from data preparation to model training, evaluation, and inference, with a focus on production environments. | PretrainServe | 8 |
| Software Engineer- AI/ML, AWS Neuron Software Engineer role focused on building and tuning distributed training solutions for AWS Inferentia and Trainium accelerators, specifically for large language models and other ML model families. The role involves working with PyTorch, Jax, XLA, and the Neuron compiler/runtime to maximize performance and efficiency on AWS Trainium. | PretrainServe | 8 |