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 62 active AI-related roles in our index. The most common open titles are: DC-GPU Performance Modeling Engineer (3), Data Center Engineer (2), Lead Packaging Automation Engineer (2), 3D IC and ADVANCED PACKAGING CAD ENGINEER , AI Framework Engineer. Most positions are in Engineering and Research.
AMD's active AI hiring is concentrated in: serving infrastructure (73%), agents (13%), application (6%). 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 (27 roles), India (13 roles), China (7 roles), Canada (6 roles).
Job postings at AMD most frequently mention: GPU Computing, Computer Architecture, Compiler Design, Python, Performance Profiling.
In the past 30 days, AMD has posted 55 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Senior Staff AI Software System Design Engineer Senior Staff AI Software System Design Engineer at AMD, focusing on custom development, debugging, optimization, and technical support of machine learning software for AMD server GPUs. The role involves working with AI frameworks, distribution, kernel operators, compilers, and runtimes, with a strong emphasis on performance optimization for inference and training workloads. Responsibilities include supporting customer Proofs of Concept (PoCs), driving custom AI software requirements from POC to release, and collaborating with various teams to optimize training and inference solutions. | ServePost-train | 8 |
| AI Software System Design Engineer AI Software System Design Engineer at AMD responsible for developing, debugging, optimizing, and supporting machine learning end-to-end custom software solutions for AMD server GPUs. This involves deep expertise in ML kernel operators, programming languages like Triton/CUDA/PTX, and development libraries, with a focus on performance optimization for inference and training workloads. The role requires strong C++ and Python skills, hands-on experience with AI use cases, pipelines, frameworks, parallel programming, and debugging. |
| ServePost-train |
| 8 |
| AI Software Development Eng. AI Software Development Engineer at AMD focused on optimizing deep learning frameworks and inference/training performance on AMD GPUs. The role involves end-to-end optimization of distributed inference and RL solutions, collaboration with internal GPU library teams and open-source maintainers, and working with cutting-edge compiler technologies and distributed computing environments. | ServeAgent | 8 |
| GPU Kernel Development Engineer GPU Kernel Development Engineer at AMD focused on optimizing deep learning frameworks (TensorFlow, PyTorch) for AMD GPUs. The role involves developing and optimizing GPU kernels, deep learning models, and improving training/inference performance across distributed systems, leveraging compiler technologies and low-level programming. Collaboration with internal GPU library teams and open-source maintainers is key. | ServePost-train | 7 |
| Sr. Software Development Engineer Senior Software Development Engineer focused on optimizing AI/ML models (CNN, Transformer, LLM, multimodal) for AMD hardware platforms. Responsibilities include developing quantization, low-precision, and compression features, building production-quality Python tools, and analyzing performance tradeoffs. Collaborates with researchers, framework engineers, and hardware experts. | ServePost-train | 7 |
| AI Framework Engineer Software engineer at AMD focused on optimizing the performance of AI inference frameworks and applications on AMD hardware. This role involves developing and validating optimization features, working with LLM frameworks like vLLM and SGLang, and contributing to AI operations libraries and kernel implementations. The engineer will also perform performance analysis using profiling tools. | Serve | 7 |
