# Principal Machine Learning Engineer, Foundation Models, AI for Drug Discovery at Genentech

- Company: Genentech
- What the company does: Genentech discovers, develops, manufactures and commercializes treatments for patients with serious and life-threatening medical conditions. Backed by North Carolina Biotechnology Center.
- Company website: https://gene.com
- Type: Startups (AI role)
- Level: Principal and up
- Location: 2 Locations
- Work setup: On-site
- Pay: $193K to $358K base salary per year (USD)
- Posted: 2026-09-08
- Apply by: 2026-10-23
- Apply: https://roche.wd3.myworkdayjobs.com/en-US/ROG-A2O-GENE/job/New-York-City/Principal-Machine-Learning-Engineer_202609-122724-1
- Page: https://www.1752.vc/careers/jobs/genentech-principal-machine-learning-engineer-foundation-models-ai-for-drug-disc/

## About the role

At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning. We are seeking a Principal Machine Learning Engineer to join our Foundation Models team. In this role, you will drive the engineering, scaling, and operationalization of our internal reasoning Large Language Models (LLMs) and agentic systems, enabling them to succeed at complex biomolecular design and autonomous scientific workflows.

## What they're looking for

- Engineering Foundation: Exceptional Python programming skills and rigorous software engineering fundamentals (Git, automated testing, CI/CD, documentation, architecture design)
- Deep Learning & Distributed Systems: Extensive hands-on experience with modern deep learning frameworks (PyTorch, JAX) and deploying ML infrastructure on AWS or HPC environments, including distributed training tools
- Agentic Systems: Practical experience designing agent orchestration frameworks (e.g., LangGraph, MCP-based tool integration), managing persistent agent memory, and building self-improving loops
- Domain Interest: Strong passion for applying frontier AI and agentic science to AI for Drug Discovery (AI4DD), biology, and chemistry
- Inference-Time Optimization: Deep expertise in LLM serving, test-time compute, sampling/search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs
- Scientific Context: Experience working with molecular modalities (e.g., protein sequences, chemical graphs, and structured molecular data)