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 |
|---|---|---|
| Compiler Engineer II - Machine Learning, Annapurna Labs The role involves developing and scaling a deep learning compiler stack for AWS Machine Learning accelerators (Inferentia and Trainium chips). The engineer will architect and implement features for the AWS Neuron SDK, focusing on making LLM and Vision models run performantly on accelerators. This includes compiler development, optimization, and integration with ML frameworks like PyTorch, TensorFlow, and JAX. | Serve | 8 |
| Post-Silicon Systems Validation Engineer, Annapurna Labs This role focuses on validating next-generation machine learning accelerators for AWS cloud infrastructure. The engineer will be responsible for the complete vertical stack, from silicon to system-to-system interfaces, ensuring the quality and performance of AI/ML accelerators. This involves developing validation strategies, executing test plans, debugging hardware, and collaborating with various engineering teams throughout the product development lifecycle. |
| ServeAgent |
| 7 |
| Software Development Engineer , Amazon Customer Service Software Development Engineer role focused on building and maintaining data infrastructure and ML platform infrastructure to support the complete AI model lifecycle, from development to production deployment, within Amazon Customer Service. | ServeData | 7 |
| Software Development Engineer, Alexa Audio Software Development Engineer role focused on integrating Generative AI and LLM capabilities into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reducing latency, and influencing operational excellence for audio services. It also includes mentoring junior engineers and contributing to hiring efforts. | ServePost-train | 7 |
| Software Development Engineer, Alexa Audio Software Development Engineer role focused on integrating Generative AI and LLM capabilities into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reducing latency, and influencing operational excellence for audio services. It also includes mentoring junior engineers and contributing to hiring efforts. | ServePost-train | 7 |
| Software Development Engineer, Intellectual Property Protection Software Development Engineer to build and maintain large-scale, low-latency, high-throughput distributed software systems that identify, prevent, and act on product listing abuse on Amazon's catalog, utilizing image processing and machine learning solutions built on AWS. | Serve | 7 |
| Software Dev Engineer II, AWS Elemental Inference Software Development Engineer II on the AWS Elemental Inference team, focused on building and shipping production code for AI-driven video processing systems. The role involves translating research into scalable services, improving performance, and implementing MLOps best practices for AI-enhanced systems. | ServeData | 7 |
| Sr. Software Development Manager - Compiler, AWS Neuron, Annapurna Labs The Sr. Software Development Manager will lead a team of compiler engineers developing, deploying, and scaling a compiler targeting AWS Inferentia and Trainium ML accelerators. This role involves deep knowledge of resource management, scheduling, code generation, and optimization for new instruction architectures, with a focus on delivering high-performance, low-cost ML inference and training solutions for AWS customers. | Serve | 7 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads. | Serve | 7 |
| ML Compiler Engineer , AWS Neuron, Annapurna Labs The AWS Neuron team is seeking ML Compiler Engineers to optimize deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia/Trainium). This role involves analyzing and optimizing system-level performance across the entire technology stack, from frameworks to runtime, and designing/implementing compiler optimizations. The position requires a passion for performance analysis, distributed systems, and machine learning, with a focus on improving the performance capabilities of the AWS Neuron SDK. | 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 |
| 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 |
| ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads. | Serve | 7 |