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: Data Center Engineer (2), Lead Packaging Automation Engineer (2), Software Development Engineer (2), AI Framework Engineer, AI Software Development Eng.. Most positions are in Engineering and Research.
AMD's active AI hiring is concentrated in: serving infrastructure (54%), agents (19%), application (14%). 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 (29 roles), India (8 roles), Poland (6 roles), China (5 roles).
Job postings at AMD most frequently reference: model serving, inference infra, fine tuning, agent orchestration, evals.
In the past 30 days, AMD has posted 68 new AI-related roles. That is a +209% change versus the prior 30 days (22 → 68).
| Title | Stage | AI score |
|---|---|---|
| Director AI End-System Performance and Customer Co-Engineering Director of AI End-System Performance and Customer Co-Engineering at AMD, leading customer engagements for successful deployment of AI on edge and physical AI platforms, focusing on system-level optimization and reference architectures for markets like robotics and digital cockpits. The role involves leading a multidisciplinary team to bridge AI models, toolchains, system software, and silicon for scalable, production-grade systems. | ShipAgent | 7 |
| Principal Software Quality Engineer – GPU & Machine Learning Principal Software Quality Engineer at AMD focusing on ROCm software validation for GPU and Machine Learning workloads. The role involves defining and owning the end-to-end validation architecture, setting release qualification gates, driving compute workload validation (including LLM training and inference), architecting test infrastructure, and leading complex debugging efforts. A key requirement is hands-on experience with agentic AI engineering environments for daily work. |
| ShipServe |
| 7 |