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The Deployment Engineer will build and operate AI inference clusters, ensure scalable deployments, optimize allocation, and maintain infrastructure. Responsibilities include software updates, telemetry development, and collaborative improvements with teams.

The Manufacturing Bring-up Engineer oversees system testing and validation, collaborating with cross-functional teams to automate workflows and enhance manufacturing efficiencies.

Support system-level bring-up processes in manufacturing, collaborate on automation and validate system performance, resolve technical issues and track metrics for continuous improvement.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

Design and develop high-performance distributed software for scalable AI training systems, focusing on data pipelines and system efficiency.

Develop and automate configuration for distributed clusters, create monitoring tools, manage cloud operations, and ensure system reliability for Cerebras AI supercomputers.

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 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.

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.