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 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 |
| Postdoctoral Scientist, Amazon Robotics R&D Postdoctoral Scientist role in Amazon Robotics R&D focusing on computer vision and robotic manipulation for automating picking operations in fulfillment centers. The role involves developing ML solutions for robots to identify and interact with items in cluttered 3D scenes in real-time, with a focus on pushing research boundaries and deploying innovations to real warehouses. | ShipData | 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 |
| Applied Scientist, Amazon Robotics R&D Applied Scientist role focused on researching, designing, and implementing computer vision and decision-making algorithms for robotic systems, with a strong emphasis on integrating machine learning and delivering customer-facing results. The role involves creating experiments, prototyping new algorithms, and collaborating with engineering teams for scalable, real-time implementations, ultimately shipping AI-powered robotic products. | ShipData | 7 |