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 Development Engineer, ML Systems, Annapurna Labs Software Development Engineer focused on ML Systems within Amazon Annapurna Labs, working on AWS Neuron software for ML chips (Inferentia and Trainium). The role involves building and applying AI agents to accelerate customer adoption of this technology, optimizing performance, durability, cost, and security for AWS customers. | Serve | 7 |
| Senior ML Kernel Performance Engineer The Annapurna Labs team at Amazon is seeking a Senior ML Kernel Performance Engineer to optimize deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). This role involves crafting high-performance kernels, pushing the boundaries of AI acceleration at the hardware-software boundary, and collaborating with customers to enable their models. The engineer will work on compiler optimizations, performance analysis, and contribute to future architecture designs. | Serve |
| 7 |
| Sr. ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs Senior ML Kernel Performance Engineer for AWS Neuron SDK, focusing on optimizing deep learning and GenAI workloads on custom ML accelerators (Inferentia, Trainium). The role involves designing and implementing high-performance compute kernels, optimizing performance at the hardware-software boundary, and collaborating with customers and internal teams on model enablement and acceleration. | Serve | 7 |
| Applied Scientist II, Prime Video - Personalization and Discovery Science Applied Scientist II at Amazon Prime Video focusing on personalization and discovery science. The role involves developing ML models for recommendation and search systems using deep learning, online learning, and optimization methods. It requires staying updated with the latest modeling techniques, publishing research findings, and applying advanced approaches like foundation models to solve cold-start problems and discover niche customer interests. The scientist will work on highly scalable page personalization solutions and collaborate with engineers and product managers. | Ship | 7 |
| Sr. Applied Scientist, Prime Video - Personalization and Discovery Science This role focuses on developing and launching end-to-end AI solutions for Prime Video's search and discovery systems, utilizing deep learning, GenAI, and reinforcement learning. The scientist will design and conduct experiments, collaborate with engineers and product managers, and publish research findings. The role is within the consumer domain, aiming to improve customer experience for millions of users. | Ship | 7 |
| Senior Machine Learning Compiler Engineer Senior Machine Learning Compiler Engineer responsible for the ground-up development and scaling of a deep learning compiler stack for Amazon's ML accelerators (Inferentia and Trainium). The role involves architecting and implementing business-critical features, optimizing neural net models for custom hardware, and integrating with ML frameworks like PyTorch and TensorFlow. | Serve | 7 |
| Software Development Engineer III, Annapurna Labs Software Development Engineer III at Amazon Annapurna Labs, focusing on building and applying AI agents to simplify and accelerate customer adoption of AWS Neuron ML chips (Inferentia and Trainium). The role involves solving complex technical problems, designing and implementing innovative software solutions, and working with external and internal customers to identify adoption obstacles and opportunities in the Generative AI space. | Agent | 7 |
| Sr. Machine Learning - Compiler Engineer III, AWS Neuron, Annapurna Labs This role is for a Sr. Machine Learning Compiler Engineer III on the AWS Neuron team, focusing on the development and scaling of a compiler for ML accelerators. The role involves architecting and implementing features for a deep learning compiler stack that optimizes neural network performance on custom AWS hardware, integrating with frameworks like PyTorch and TensorFlow. The goal is to provide significant performance improvements for large-scale ML workloads. | Serve | 7 |
| Software Development Manager - ML Performance Tooling and Benchmarking, AWS Neuron, Annapurna Labs Manager III leading a team of compiler engineers to develop, deploy, and scale a compiler targeting AWS Inferentia and Trainium ML accelerators. The role involves technical leadership, innovation, and collaboration with AWS ML services teams to ensure the Neuron SDK meets customer needs for high performance, low cost, and ease of use. Deep knowledge of resource management, scheduling, code generation, and optimization is required. | Serve | 7 |
| Sr. SDE, MLA hardware/software co-design, Annapurna Labs Machine Learning Acceleration Senior Software Development Engineer focused on pre-silicon hardware/software co-development for next-generation machine learning chips (like Trainium) used in AWS. The role involves working with architecture, design, and emulation teams, writing bare-metal software and ML workloads to verify chip functionality and performance. | Data | 7 |
