Performance Modeling Engineer

Highlights

• Develop performance models for next-gen AI inference architecture.
• Co-optimize hardware and software to unlock throughput and latency gains.
• Pathfind system and silicon options to shape future chip decisions.

What they offer

• Medical, dental, and vision packages with generous premium coverage
• $500 per month credit for waiving medical benefits
• Housing subsidy of $2.5k per month for those living within walking distance
• Relocation support for those moving to San Jose
• Wellness benefits covering fitness, mental health, and more
• Daily lunch + dinner in office
• Unlimited compute budget subject to ROI justification

Ideal candidate

You are a systems-minded performance thinker who loves modeling complex architectures and hunting bottlenecks in AI workloads to guide hardware-software tradeoffs and future designs.

Requirements

• Strong performance modeling and analysis skills with experience building analytical-based or simulation-based performance models
• Solid understanding of computer architecture and micro-architecture, particularly for accelerators
• Experience profiling and analyzing deep learning workloads on hardware accelerators (GPUs, TPUs, ASICs, FPGAs, or others)
• Solid software engineering fundamentals with an eye toward auditability and maintainability
• Deep knowledge of GPU architectures and/or programming models like CUDA
• Experience mapping models to multi-chip inference systems
• Familiarity with transformer model architectures and inference serving optimizations
• Experience with architecture simulators and performance modeling tools (gem5, trace-driven simulators, custom models)
• Exposure to ASIC, FPGA, or CGRA-based accelerator development and hardware/software co-design principles
• Published research in computer architecture, ML systems, or hardware acceleration