Deep Learning Performance Architect - New College Grad 2027

Responsibilities

• Build or improve pre-silicon performance and system-level models to better understand workload behavior, architecture tradeoffs, and projection accuracy
• Develop correlation methodologies to compare model projections with lower-level analyses or available reference data and improve model fidelity
• Study how GPU microarchitecture and system-level design choices affect end-to-end performance, efficiency, scalability, and projection fidelity
• Identify performance bottlenecks across model execution, kernels, data movement, and system behavior
• Explore and evaluate architecture ideas that improve performance, efficiency, or model fidelity
• Collaborate with kernel, hardware, and framework teams to better understand end-to-end behavior for LLMs, agentic AI systems, and other generative AI models
• Use profiling and benchmarking tools as needed to support model development, analysis, and correlation
• Present findings and insights through clear analysis and visualization
• What we need to see
• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience

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