Deep Learning Performance Architect - New College Grad 2027

What you'll do

• Build and improve pre-silicon performance and system-level models, including correlation methods.
• Analyze GPU microarchitecture, system design, and performance bottlenecks across workloads, kernels, and data movement.
• Evaluate architecture ideas and collaborate with kernel, hardware, and framework teams; present findings.

What they ask for

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
• Good programming skills in C++ and Python, with solid software engineering fundamentals.
• Basic understanding of modern deep learning models such as Transformers or LLMs.
• Exposure to computer architecture, parallel programming, performance analysis, correlation, system modeling, or related systems topics.

Nice to have

• Experience with CUDA programming, GPU performance analysis, performance modeling, correlation, system-level analysis, or profiling tools.
• Hands-on work with performance-sensitive systems, computer architecture, or ML infrastructure through coursework, projects, internships, or research.

Pay

124,000 USD - 195,500 USD base salary (Level 2); 152,000 USD - 241,500 USD base salary (Level 3)

Tools

C++, Python, PyTorch, TensorRT-LLM, Triton, Nsight, vLLM, CUDA

AI focus

Performance modeling and architecture analysis for LLM inference, agentic AI systems, and other generative AI workloads.