Principal ML Engineer - Foundation Models Drug Discovery

Genentech; South San Francisco (CA); On-site; USD 193,000 - 358,000; Full time

Job summary

Genentech, a Roche subsidiary, is seeking a Principal Machine Learning Engineer for the Foundation Models team in a role focused on building and scaling autonomous, agentic AI systems for drug discovery. You will own the MLOps/AgentOps stack, drive production-grade pipelines, and guide the engineering roadmap for AI4DD’s foundation models.

You will work at the intersection of research and engineering, deploying large-scale ML infrastructure on AWS/HPC, and collaborating with ML scientists and

Qualifications

• BS/MS/PhD in CS or related quantitative field
• Experience shipping large-scale ML systems
• Strong Python programming and CI/CD fundamentals

Responsibilities

• Own MLOps/AgentOps stack for Foundation Models and establish evaluation harnesses, experiment tracking, observability, CI/CD and infrastructure
• Architect and deploy autonomous agents with tool access and long-horizon reasoning across drug-discovery workflows
• Design, build and optimize large-scale distributed training and inference systems; own production Python/PyTorch codebases
• Define long-term engineering roadmap for AI4DD’s agentic and foundation models; provide technical leadership
• Translate open-ended scientific problems into scoped, efficient, deployable systems

Skills

Python

Software engineering

Distributed systems

ML deployment

Git CI/CD

Education

PhD in Computer Science

MS in Computer Science

BS in CS

Tools

PyTorch

JAX

AWS

HPC environments

Job description

Genentech, a Roche subsidiary, is seeking a Principal Machine Learning Engineer for the Foundation Models team in a role focused on building and scaling autonomous, agentic AI systems for drug discovery. You will own the MLOps/AgentOps stack, drive production-grade pipelines, and guide the engineering roadmap for AI4DD’s foundation models.

You will work at the intersection of research and engineering, deploying large-scale ML infrastructure on AWS/HPC, and collaborating with ML scientists and