AMD currently has 35 active AI-related job listings. The majority of these roles, 66%, are focused on serving infrastructure. Engineering is the dominant function, with 32 positions. Frequent technical tags include model_serving, inference_infra, and evals, suggesting a focus on the deployment and evaluation of AI models. In the last 30 days, AMD posted 37 new AI roles.
AMD currently has 59 active AI-related roles in our index. The most common open titles are: DC-GPU Performance Modeling Engineer (3), Sr. Software Development Engineer (3), Data Center Engineer (2), MTS Software Development Engineer (2), Software Development Engineer (2). Most positions are in Engineering and Research.
AMD's active AI hiring is concentrated in: serving infrastructure (78%), data (10%), agents (5%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
AMD is hiring AI talent in: United States (30 roles), India (13 roles), Poland (5 roles), China (5 roles).
Job postings at AMD most frequently mention: GPU Computing, Computer Architecture, Python, C++, PyTorch.
In the past 30 days, AMD has posted 70 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| 3D IC and ADVANCED PACKAGING CAD ENGINEER This role focuses on integrating AI and ML capabilities into CAD and EDA workflows for 3D-IC and advanced packaging design. The engineer will develop and deploy agentic AI systems, LLM-assisted automation, and ML-based optimizations to improve design quality of results (QoR) in areas like timing, area, and performance. The role requires a strong silicon hardware design background, software/scripting proficiency, and hands-on experience applying AI to CAD/EDA problems. | Agent | 7 |
| PMTS Software Applications Eng. This role is for a Principal Member of Technical Staff (PMTS) Software Development Engineer in AMD's AI Enterprise applications and platform engineering organization. The engineer will design, build, and evolve secure, reliable AI infrastructure applications and tools for deploying, running, and scaling ML and LLM workloads on cloud and GPU infrastructure. The role involves full-stack engineering, technical and strategic leadership, influencing roadmap and architecture, and partnering with product and customers. Key responsibilities include owning backend services and user-facing applications for AI/GPU and platform workflows, setting architectural direction for Kubernetes toolchain, optimizing systems for efficiency and reliability, and embedding engineering excellence. The role also involves customer interaction, leading initiatives from requirements to shipped outcomes, and mentoring engineers. Experience with GPU workloads, ML/LLM training or inference pipelines, and multi-tenant platform concerns is highly desirable. |
| ServeAgent |
| 7 |