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

The Opportunity

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. The successful candidate will contribute to the design, development, and scaling of large-scale foundation models and AI agents, with the ultimate aim of accelerating target and drug discovery. This role spans the full stack: the agent design and orchestration logic that makes these systems scientifically useful, and the infrastructure, AgentOps, and MLOps that make them robust, reproducible, and efficient at scale.

In this role, you will:

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

Who you are

• 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

• 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)