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 II, AFT AI, Amazon AFT AI Applied Scientist II role focused on developing and deploying agentic AI solutions and multi-modal deep learning models for Amazon's Fulfillment Network. The role involves working with large-scale, real-world datasets (imagery, natural language, structured data) to solve complex problems like warehouse operations and visual defect detection, pushing the state-of-the-art in optimizing fulfillment systems. | AgentPost-train | 9 |
| Applied Scientist III, AFT AI, Amazon AFT AI Develop agentic AI and multi-modal deep learning models for Amazon's Fulfillment network, focusing on understanding warehouse operations and visual defect detection. This role involves working with large, diverse datasets and applying cutting-edge AI techniques to solve complex, real-world problems at scale, with a strong emphasis on production deployment and iterative improvement. |
| AgentPost-train |
| 9 |
| Applied Scientist Applied Scientist role at Audible focusing on developing and productionizing ML/AI models for various applications including NLP, RL, and Generative AI. The role involves inventing scientific approaches, building scalable solutions, and collaborating with engineering and product teams to deliver customer-facing features and foundational capabilities. | Ship | 8 |
| Head of Applied Science The Head of Applied Science at Audible will manage research teams, drive technical vision and strategy, and translate complex business requirements into production-ready ML solutions. This role involves leading the full development cycle from research to maintenance, evaluating new ML technologies, and collaborating across science, engineering, and product teams to deliver customer-facing AI products. | Ship | 8 |
| Applied Science Manager, AWS Generative AI Innovation Center Manager for an AWS Generative AI Innovation Center focused on building and delivering generative AI solutions for customers, managing a team of scientists and engineers, and driving adoption and strategic relationships. | ShipPost-train | 8 |
| Applied Scientist, EU International Technologies Applied Scientist role focused on improving Amazon's product search service by developing and deploying ML solutions for query understanding, ranking, and personalization. The role involves analyzing data, designing and building ML models, evaluating them through offline and online tests, and publishing research. It requires knowledge of ML fundamentals, transformer architectures, and training/inference lifecycles, with a focus on delivering customer-facing improvements in a production environment. | ShipServe | 7 |
| Software Dev Engineer Intern Machine Learning, Amazon Robotics Software Development Engineer Intern Machine Learning, Amazon Robotics. The role involves simplifying ML training infrastructure, extending monitoring of ML trainings, and potentially developing perception models for warehouse robots. It requires experience with AI, Neural Networks, Tensorflow, and PyTorch, and focuses on building scalable ML solutions within a robotics context. | Data | 7 |
| Sr. Robotics Software Engineer, Amazon Robotics This role involves architecting, designing, and implementing robotic software applications and infrastructure, with a focus on integrating machine learning models and optimizing system performance for Amazon's robotic systems. The engineer will collaborate with various teams to deliver end-to-end robotic solutions. | Agent | 7 |