ML Systems Performance Engineer
Cerebras Systems · Sunnyvale CA or Toronto Canada · Software
About this role
Cerebras Systems is hiring a mid-level Systems Engineer in the operations function based in Sunnyvale CA or Toronto Canada. The posting calls out experience with Python, C#, LLMs, Deep Learning and roughly 3+ years of relevant work. Listed education preference: a bachelor's degree or equivalent.
- Role
- Systems Engineer
- Function
- operations
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Sunnyvale CA or Toronto Canada
- Experience
- 3+ years
- Education
- Bachelor's degree
- Department
- Software
More roles at Cerebras Systems
Job description
from Cerebras Systems careersCerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
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.