Principal ML Investigator
Leads new ML research team focusing on post-training/RL, dataset optimization, LLM pretraining, sparsity, and domain-specific agents. Adapts algorithms for Cerebras hardware, builds teams, and collaborates on hardware/software design. Requires PhD and ML leadership experience.
About the job
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.
Skills & Qualifications
- PhD in Computer Science or related field.
- Strong grasp of ML theory in one or more of the following areas: post-training and reinforcement learning, dataset curation and optimization, LLM pretraining, sparsity, domains (coding agents, reasoning agents, generative language, image, video).
- Proven experience engineering ML systems for scale or production deployment.
- Experience leading a team of researchers or engineers.
Preferred Skills & Qualifications
- 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.
Skills
Reinforcement Learning, Llm Pretraining, Dataset Curation, Sparsity Techniques, Distributed Training, LLMs, Ml Systems Engineering, Triton, Model Optimization, Production Ml Deployment
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