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The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Join the Advanced Technology Group as a Compiler Engineer to design and develop the Tungsten language compiler for wafer-scale hardware, optimizing code generation for AI and scientific workloads.

The Senior Hardware Technical Program Manager will lead program schedules for engineering improvements, collaborating across teams for hardware integration and deployment in AI supercomputers.

The role involves collaborating with architects and engineers to develop and implement verification strategies for complex AI chip designs, ensuring high-quality verification processes and methodologies.

Lead design and implementation of a globally distributed inference orchestration platform. Drive platform direction, reliability, performance, and production incident leadership. Write and review production code, make high-consequence architectural decisions, and mentor senior engineers while partnering with ML, product, infrastructure, and cloud teams.

Build and lead the inference orchestration layer for a globally distributed ML inference platform. Architect and implement highly available, low-latency systems, own reliability and performance, write production code, drive incident response, observability, capacity planning, and collaborate with ML, infrastructure, and cloud teams on scalable designs.

Join Cerebras Systems to optimize and develop frontend compiler infrastructure for PyTorch models on their unique wafer-scale AI platform, focusing on model representation and optimization.

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.

Manage and curate the AI models portfolio, collaborate on model launches, evaluate model quality, and drive product marketing initiatives.

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