AI Performance Engineer – GPU & ROCm

We are looking for a GPU AI Performance Engineer to optimize and deploy AI and machine-learning workloads using ROCm.

You will improve the speed, memory efficiency, and scalability of AI training and inference workloads. You will also work with GPU software, AI frameworks, profiling tools, and cloud or containerized environments.

• Improve training speed, inference latency, memory usage, and multi-GPU scalability.
• Work with large language models, vision models, multimodal AI, and generative AI.
• Develop and troubleshoot applications using ROCm and HIP.
• Work with PyTorch, Tensor Flow, ONNX Runtime, vLLM, SGLang, or MIGraph
  
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• Profile GPU applications and identify performance bottlenecks.
• Apply mixed precision, quantization, kernel tuning, operator fusion, and memory optimization.
• Support AI deployment on Linux, cloud, edge, and containerized platforms.
• Develop performance benchmarks and automated validation tests.
• Collaborate with hardware, compiler, runtime, framework, and infrastructure teams.

• Bachelor’s or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
• Strong programming skills in C++ and Python.
• Experience with Linux and GPU computing.
• Hands‑on experience with ROCm, HIP, CUDA, or similar GPU technologies.
• Understanding of GPU architecture, parallel programming, and memory management.
• Experience with PyTorch, Tensor Flow, ONNX Runtime, or similar AI frameworks.
• Experience profiling and optimizing GPU applications.
• Knowledge of AI model training, inference, and performance optimization.

• Experience optimizing large language models or generative AI applications.
• Experience with vLLM, SGLang, or distributed inference.
• Familiarity with LLVM, MLIR, or compiler technologies.
• Experience migrating workloads between CUDA and HIP.
• Knowledge of Kubernetes, containers, and distributed AI systems.
• Contributions to ROCm or other open-source GPU projects.