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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 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.

Develop high-performance code for the distributed system, optimizing data structures and algorithms for Cerebras' architecture in AI applications.

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.