Jobs at Cerebras Systems

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

As a Performance Reliability Engineer, you will optimize performance and reliability of ML systems, analyze workloads, enhance collaboration with cross-functional teams, and influence architecture design.

Join the Inference Core Model Bringup team to bring up ML models on Cerebras CSX systems, focusing on performance, optimization, and debugging.

As a Performance Engineer, you will optimize CPU and memory subsystems for high-performance ML workloads on x86 machines, develop algorithms for data movement, and engage with the AI community to enhance our AI platform.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

The Applied Machine Learning Research Scientist will translate ML techniques into scalable systems, improve LLM performance, and optimize workflows while collaborating closely with researchers and engineers.

Assist in deploying and monitoring Cerebras AI infrastructure, perform troubleshooting, collect telemetry, and learn through shadowing senior engineers.

Join the Kernel Reliability team to improve the reliability of compute clusters, assist in debugging, and enhance system tools alongside hardware teams.

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.

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.

Design and architect front-end network fabrics for AI/ML and HPC clusters. Automate deployment of network infrastructure, debug networking issues, and lead technical projects across teams.

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

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