Evaluate, benchmark, profile, and optimize AI inference workloads across modern CPU, GPU, APU, and heterogeneous computing platforms.
- Benchmark and characterize AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related technologies.
- Analyze latency, throughput, memory utilization, power consumption, and performance per watt.
- Profile workloads and identify bottlenecks across AI frameworks, runtimes, operating systems, memory subsystems, and hardware.
- Optimize workload performance through quantization, graph optimization, kernel tuning, memory optimization, and heterogeneous compute utilization.
- Conduct competitive benchmarking across AMD and competing CPU, GPU, APU, and AI accelerator platforms.
- Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related field
- Nice to have: ROCm
- Nice to have: Ryzen AI Software
- Nice to have: Vitis AI
- Nice to have: MIGraphX
