GPU AI Performance Engineer

• Analyze and evaluate GPU architecture/microarchitecture for performance optimizations of AI workloads

• Work across the AI stack, from model graphs and inference runtimes down to GPU kernels and compiler IR

• Collaborate with hardware, software, and ML teams to identify performance bottlenecks and propose architectural or algorithmic optimizations

• Analyze AI workload characteristics and correlate their behavior across different GPU generations

• Contribute to architectural trade-off studies and influence GPU roadmap decisions with data-driven insights

Preferred Skills:

• Strong understanding of CPU/GPU architecture

• Experience in Python, C++, and ML frameworks (e.g., TensorFlow, PyTorch)

• Skills: C/C++ Programming Language, Scripting (Python/Perl), Assembly, Verilog/SystemVerilog

• Familiarity with AI inference runtimes and deployment stacks for model compilation, optimization, and execution on GPU/accelerator platforms

• Good understanding of common neural network layers and operations, including what they do and how they affect model behavior and performance

Nice to have:

• Experience with LLM inference engines such as llama.cpp

• Exposure to Triton, TTIR, TTGIR, or GPU compiler pipelines

• Knowledge of quantization formats and tradeoffs

• Understanding of MoE architectures

• Experience with GPU driver and compiler development

• Experience with OpenCL or Cuda development

Minimum Qualifications:

•    Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience.
OR
Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Systems Engineering or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.