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Lead a team to improve the reliability of advanced compute clusters and manage failure analysis and debugging processes.

As an Engineering Manager for Inference ML Runtime, you will lead a team to design, scale, and optimize AI inference systems on Cerebras hardware, ensuring high performance and overseeing execution of ML engineering initiatives.

The role involves innovating tests for AI infrastructure, automating test strategies, ensuring high reliability and security for large-scale deployments, and understanding distributed ML systems. Candidates must have strong coding and debugging skills in a team focusing on AI technology.

Lead a team in developing and scaling distributed inference systems for AI on Cerebras' hardware, ensuring reliability and performance while mentoring engineers.

Design, implement, optimize, and validate high-performance ML and linear algebra kernels for Cerebras hardware. Develop low-level assembly and CSL routines, measure and tune performance, build testing methodologies, and collaborate with chip and system architects to maximize compute utilization for AI and HPC workloads.

The Engineering Lead will build a UI-based large-scale management portal for Cerebras clusters, ensuring seamless integration with backend systems and technical leadership. Responsibilities include mentoring a team, collaborating with product management, and delivering a user-friendly tool.

The Deployment Engineer will manage AI inference clusters, optimizing deployment, capacity allocation, and ensuring reliability of pipeline operations across datacenters.

Manage AI compute clusters, monitor systems health, optimize resources, and troubleshoot technical issues, ensuring high performance for ML applications.

You will prototype and benchmark model innovations, develop automation for experiments, and work with teams on software/hardware integration.

The role involves developing infrastructure for benchmarking AI inference performance, analyzing system behavior, designing telemetry systems, and collaborating with teams to improve feature performance.