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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.

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 our kernel micro code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras WSE.
- Debug and understand runtime performance on the system and cluster.
- Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.

Requirements

- 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.

Equal Opportunity

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal prospect employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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Apply on Kit Job: kitjob.ca/job/2fslxe

📌 ML Systems Performance Engineer (Toronto)
🏢 Cerebras
📍 Toronto