Senior Machine Learning Engineer, Agentic Science/Generative Models, AI for Biology & Translation (AIBT)

About the role

A healthier future drives us to innovate, continuously advance science, and ensure everyone has access to the healthcare they need. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) leverage AI, data, and computational sciences to accelerate drug discovery and development. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group harnessing the transformative power of data and AI to assist scientists in delivering innovative medicines for patients worldwide.

The AI Biology & Translation (AIBT) department within Genentech's Computational Sciences Center of Excellence (CS-CoE) is building the next generation of AI systems for biology. Our mission is to develop AI models that learn from biological data at unprecedented scale, generating new insights into disease mechanisms, therapeutic opportunities, and human biology. We seek a highly motivated and passionate Senior ML Engineer to join our Generative Modeling team and help build and scale foundation models and agentic systems for therapeutic discovery.

Responsibilities

• Agentic systems
  • Build agents that use tools, retrieve evidence, and reason across multi-step scientific workflows
  • Build reliable interfaces between agents and biological, genomic, and clinical data sources
  • Design evaluation harnesses that check agent output against scientific ground truth
  • Design and implement self-improving and autonomous loops for autoML and lab in the loop
  • Implement agent memory and context management for long-horizon workflows
• Models and production systems
  • Build, finetune, deploy, and scale foundation models and LLMs in production
  • Own production Python/PyTorch (or JAX) codebases that turn fast-moving research ideas into reliable, reusable software
  • Own the MLOps/AgentOps lifecycle: experiment tracking, evaluation, monitoring, reproducibility, CI/CD, and infrastructure-as-code (Terraform, Helm, Kubernetes)
• Collaboration
  • Work with research scientists to turn open-ended scientific problems into scoped, shippable systems
  • Raise the engineering bar across gRED and Roche

Requirements

• BS/MS in CS, ML, engineering, or a related quantitative field
• 5+ years building and shipping ML systems in industry
• Excellent Python; strong software and data engineering fundamentals (Git, automated testing, CI/CD, documentation)
• Track record leading technical projects end to end
• Comfort with ambiguity and close collaboration with scientists
• Strong problem-solving and communication skills
• Interest or experience in applying ML to scientific discovery (AI for science), such as biology, chemistry, or drug discovery, including working with domain-specific data and models

Preferred Qualifications

• Inference-time scaling and optimization: test-time compute, sampling and search strategies, model routing, batching, caching, latency/cost/quality tradeoffs
• ML infrastructure on AWS (EC2, S3, EKS, SageMaker), including distributed training and inference on HPC
• Evaluation systems for agentic applications where correctness is scientifically defined
• Agent orchestration frameworks in production (LangGraph, MCP-based tool integration)

Pay

The expected salary range for this position based on the primary location of San Francisco is $168,100 - $312,300. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance.

Benefits

This position qualifies for the benefits detailed at the provided link.