Senior Staff AI Accelerator Performance Architect

Highlights

• Guide next-gen wafer-scale AI systems via rigorous performance modeling.
• Own models connecting real workloads to architectural behavior and bottlenecks.
• Influence hardware and software roadmaps with quantified improvement analysis.

What they offer

• bonus
• equity

Ideal candidate

Deep hardware thinker who loves connecting real AI workloads to architecture. You enjoy modeling, quantifying bottlenecks, and shaping future systems through clear, rigorous analysis.

Requirements

• 7+ years of experience in performance analysis, performance modeling or architecture exploration for CPUs, GPUs, AI accelerators or other high-performance computing systems.
• Strong understanding of hardware architecture developed through hardware, compiler, kernel, runtime or system-performance work.
• Experience developing analytical, simulation-based or trace-driven performance models using Python, C++ or similar environments.
• Solid understanding of processor architecture, memory systems, interconnects, parallel execution and hardware resource constraints.
• Ability to move between kernel-level behavior and end-to-end application or system performance.
• Experience profiling workloads, forming performance hypotheses and validating them with quantitative evidence.
• Understanding of how software mapping and programmability affect realized hardware performance.
• Ability to communicate modeling assumptions, uncertainty, bottlenecks and recommendations clearly.
• MS or PhD in Electrical Engineering, Computer Engineering, Computer Science or equivalent practical experience.