Principal ML Investigator

Cerebras is adding an ML team that can focus on a new ML effort that can align with existing teams. We are seeking a principal investigator who will partner with our ML leaders to formulate the new effort and to build up the new team and capabilities. This new team would coordinate with our current ML teams: Field ML, which works directly with customers, Applied ML, which builds new ML capabilities and applications for customers, and Core ML, which adapts ML algorithms to find unique capabilities of Cerebras hardware. The new team could take up the same or complementary responsibilities.

We would like the new team to work on some of the following areas:

• Post-training and reinforcement learning: Techniques used to improve model deployment quality through further training, tuning, RL, and focus on particular downstream tasks;
• Dataset curation and optimization: Techniques to collect and select high-quality data, which can help models to train or tune more quickly or to higher quality;
• LLM Pretraining: Techniques to ensure stability and compute-efficiency while pretraining high quality models. May include training dynamics, parameterizations, numerics, or others;
• Sparsity: Techniques to sparsify models or data that improve training time-to-quality, or optimize inference speed or throughput;
• Domains: Coding agents, reasoning agents, generative language, image, video.

Principal Investigator Responsibilities

• Build up a team capable of industry research and advanced development.
• Organize various advanced development topics into cohesive agenda.
• Adapt novel algorithms and model architectures to run on the Cerebras platform.
• Systematically train, tune, and evaluate models to guide/advise production scenarios.
• Collaborate with other teams to co-design next-generation hardware and software architectures.
• Collaborate with external partners (customers, academic) to drive insight and credibility.

• PhD in Computer Science or related field.
• Strong grasp of ML theory in one or more of the above areas.
• Proven experience engineering ML systems for scale or production deployment.
• Experience leading a team of researchers or engineers.

• Track record of patents or publications in top-tier conferences or journals.
• Experience with large language models (e.g., GPT family, Llama).
• Experience with distributed training concepts and frameworks.
• Experience in training speed optimizations, such as model architecture transformations to target hardware, or low-level kernel development (e.g., Triton).
• Ability to analytically model or optimize system performance.