ML Systems Performance Engineer

About the role

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 optimization, system-level performance analysis, performance modeling and estimation, and the development of tooling for performance projection and diagnostics.

Key responsibilities

• Build performance models (kernel-level, end-to-end) to estimate the performance of state‑of‑the‑art and customer ML models.
• Optimize and debug kernel micro‑code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras Wafer Scale Engine.
• Debug and understand runtime performance on the system and cluster.
• Develop tools and infrastructure to visualize performance data collected from the Wafer Scale Engine and the compute cluster.

Required profile

• Bachelors / Masters / 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 Optimization, HPC).
• Experience working on CPU/GPU simulators.
• Exposure to performance profiling and debug on any system pipeline.
• Comfort with C++ and Python.

Required skills

• C++
• Python

What we offer

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting‑edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Simple, non‑corporate work culture that respects individual beliefs.