Principal ML Investigator

Cerebras Cerebras · Semiconductors · Headquarters +1 · Machine Learning Departments

Seeking a Principal ML Investigator to lead a new ML team focused on LLM pretraining, post-training, dataset curation, and sparsity techniques. This role involves building a team, formulating research agendas, adapting algorithms to Cerebras hardware, training/tuning/evaluating models, and collaborating with internal and external partners. The ideal candidate has a PhD, strong ML theory, experience engineering ML systems, and leadership experience.

What you'd actually do

  1. Build up a team capable of industry research and advanced development.
  2. Organize various advanced development topics into cohesive agenda.
  3. Adapt novel algorithms and model architectures to run on the Cerebras platform.
  4. Systematically train, tune, and evaluate models to guide/advise production scenarios.
  5. Collaborate with other teams to co-design next-generation hardware and software architectures.

Skills

Required

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

Nice to have

  • 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

What the JD emphasized

  • PhD in Computer Science or related field
  • Proven experience engineering ML systems for scale or production deployment
  • Experience leading a team of researchers or engineers

Other signals

  • Formulate new ML effort and build up new team and capabilities
  • Work on LLM Pretraining, Post-training and reinforcement learning, Dataset curation and optimization, Sparsity
  • Adapt novel algorithms and model architectures to run on the Cerebras platform
  • Systematically train, tune, and evaluate models
  • Collaborate with external partners (customers, academic)