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.

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

Lead complex, cross-functional programs in AI training and inference platforms, aligning stakeholders and managing risks while improving execution.

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.

The Cybersecurity GRC Engineer will enhance compliance processes and build compliant tech solutions, integrating AI for efficiency and risk management.

As an SDET for the ML API features team, you will test AI/ML models for accuracy and performance, develop tests, and ensure quality during integration and pre-deployment validation.

Lead the design and evaluation of storage solutions for AI and HPC deployments, ensuring alignment with performance and security standards while collaborating with cross-functional teams.

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 role involves designing and developing open-source libraries and demo applications that showcase Cerebras' AI Inference capabilities, while contributing to engineering blogs and collaborating with partner teams.

Design and develop core backend services and APIs for the Cerebras Inference Platform, ensuring ease-of-use, robustness, and reliable performance for ML workloads.

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

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

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

As a Senior Research Engineer, you will optimize language and vision models on Cerebras hardware, focusing on high-performance ML inference techniques.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

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.