Principal ML Engineer: Foundation Models for Drug Discovery

Genentech; New York (NY); On-site; USD 193,000 - 358,000; Full time

Benefits offered by this job

Discretionary annual bonus

Job summary

Genentech, part of Roche, seeks a Principal Machine Learning Engineer for the Foundation Models team. You will drive the engineering, scaling, and operationalization of internal reasoning LLMs and agentic systems to support complex biomolecular design and scientific workflows.

Work spans from agent orchestration to distributed infrastructure and MLOps/AgentOps, with collaboration across ML scientists and domain experts to translate open-ended problems into efficient production systems.

Qualifications

• BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related field.
• Proven leadership with increasing levels of experience; PhD with 5+ years, MS with 8+ years, or BS with 10+ years in large-scale ML systems.
• Strong Python programming and rigorous software engineering fundamentals (Git, automated testing, CI/CD, documentation, architecture design).

Responsibilities

• Own the MLOps/AgentOps Stack for Foundation Models.
• Architect and deploy autonomous agents that utilize tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows.
• Design, build, and optimize large-scale distributed training and inference systems for foundation models.
• Define the long-term engineering roadmap for AI4DD’s agentic and foundation models.
• Partner closely with ML Scientists and domain experts to translate scientific problems into scalable, production-ready systems.

Skills

Python programming

Software engineering fundamentals

Distributed systems

Git

CI/CD

Education

BS/MS/PhD in CS/ML/Engineering

Tools

PyTorch

JAX

AWS

HPC environments

Job description

Genentech, part of Roche, seeks a Principal Machine Learning Engineer for the Foundation Models team. You will drive the engineering, scaling, and operationalization of internal reasoning LLMs and agentic systems to support complex biomolecular design and scientific workflows.

Work spans from agent orchestration to distributed infrastructure and MLOps/AgentOps, with collaboration across ML scientists and domain experts to translate open-ended problems into efficient production systems.