ML Systems Performance Engineer

Engineers on the inference performance team operate at the intersection of hardware and software, driving end-to-end model inference speed and throughput. Their work spans low-level kernel performance debugging and optimisation, system-level performance analysis, performance modelling and estimation, and the development of tooling for performance projection and diagnostics.

Responsibilities:

• Build performance models (kernel-level, end-to-end) to estimate the performance of state-of-the-art and customer ML models.
• Optimise and debug our kernel microcode and compiler algorithms to elevate ML model inference speed, throughput and compute utilisation on the Cerebras WSE.
• Debug and understand runtime performance on the system and cluster.
• Develop tools and infrastructure to help visualise performance data collected from the Wafer Scale Engine and our compute cluster.

Requirements:

• Bachelor's / Master's / PhD in Electrical Engineering or Computer Science.
• Strong background in computer architecture.
• Exposure to and understanding of low-level deep learning / LLM math.
• Strong analytical and problem-solving mindset.
• 3+ years of experience in a relevant domain (Computer Architecture, CPU/GPU Performance, Kernel Optimisation, HPC).
• Experience working on CPU/GPU simulators.
• Exposure to performance profiling and debugging on any system pipeline.
• Comfort with C++ and Python.