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 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 |
| 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. 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 |
| 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 |