Principal ML Investigator - AI Systems on Fast Hardware

Cerebras; Sunnyvale (CA); On-site; USD 250,000 - 450,000; Full time

Job summary

Cerebras Systems is expanding its ML team to explore new efforts that align with our core platforms. We are seeking a principal investigator who will partner with ML leaders to define the new program, recruit top researchers, and build a capable team to tackle cutting-edge workloads on Cerebras hardware.

The role will coordinate with Field ML, Applied ML, and Core ML teams, drive research in post-training, dataset optimization, LLM pretraining, sparsity, and domain-specific applications, and

Qualifications

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

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

ML research

Team leadership

System engineering

Cross-functional collaboration

Education

PhD in Computer Science or related field

Tools

Triton

Distributed training frameworks

LLMs (GPT, Llama)

Job description

Cerebras Systems is expanding its ML team to explore new efforts that align with our core platforms. We are seeking a principal investigator who will partner with ML leaders to define the new program, recruit top researchers, and build a capable team to tackle cutting-edge workloads on Cerebras hardware.

The role will coordinate with Field ML, Applied ML, and Core ML teams, drive research in post-training, dataset optimization, LLM pretraining, sparsity, and domain-specific applications, and