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 |
|---|---|---|
| Senior Agentic System & Application Engineer Senior Engineer to build agentic AI systems for HPC and GPU workloads, automating setup, job composition, debugging, and optimization. Role involves agent architecture, LLM inference/serving, HPC integration, and performance tooling, with a focus on correctness, reproducibility, and observability. Requires shipping production agentic systems and LLM inference/serving. | AgentServe | 9 |
| Senior LLM Inference Performance Engineer Senior LLM Inference Performance Engineer at AMD, focusing on performance analysis and optimization of production-grade AI services within the AIM ecosystem. Responsibilities include measuring, analyzing, and optimizing LLM/AI service performance (latency, throughput), designing performance measurement methodologies, and developing tooling for AI model deployment analysis and performance tuning at scale. Requires experience with LLM serving, performance metrics, Python, and containerization technologies like Docker and Kubernetes. |
| Serve |
| 8 |
