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As Principal Product Manager for ML Training at Cerebras Systems, you will design and lead the development of the ML training ecosystem, ensuring a seamless interface for researchers to preprocess, train, fine-tune, and evaluate large ML models on Cerebras chips. You'll drive the product roadmap for CSTorch and Model Zoo, collaborate with technical teams, and focus on reducing the time-to-insight for ML research.

As a Distributed Software Engineer, you will automate configuration, develop orchestration systems, and ensure robust monitoring for large-scale AI Supercomputers in a cluster environment. This role involves working with various software and tools to minimize downtime and enhance resource management in both on-premise and cloud deployments.

As a hardware-focused Data Engineer at Cerebras, you will develop and maintain reliable data processing pipelines, analyze and manage large volumes of data, and collaborate with engineering teams to drive data-driven decision-making that enhances hardware manufacturing processes.

Develop and maintain system administration and monitoring services. Collaborate on defining requirements and integrate solutions across software and hardware domains. Write quality, testable code, and optimize database systems. Proficiency in Go and C/C++ is essential.

You will be responsible for silicon testing, repair, and burn-in of Cerebras wafer scale engines. Key tasks include collaborating on silicon testing, utilizing redundancy for repairs, running algorithms to optimize wafer performance, debugging production issues, and recommending solutions based on root cause analysis.

The Senior Product Manager for the Cloud Console will define and execute the product strategy for the administrative web application that manages inference and training services. Responsibilities include conducting user research, translating user requirements into specifications, collaborating with cross-functional teams, and communicating product updates to stakeholders.

As a Senior ML Quality Engineer, you will ensure the quality of software across various ML workloads and workflows by developing testing practices, validating model training accuracy, and debugging components. You will automate workflows and contribute significantly to product quality within a fast-paced environment.