Summary

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. Depending on team coverage at a given time, you may own infrastructure end-to-end or partner with platform engineering on it, this role needs someone comfortable doing either. You'll join a multidisciplinary environment alongside ML scientists, ML engineers, and computational biologists. The ideal candidate combines strong software and ML engineering skills, a systems mindset, fluency in how agentic systems are actually built and evaluated, and a "get-it-done" attitude.

Job description

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. Depending on team coverage at a given time, you may own infrastructure end-to-end or partner with platform engineering on it, this role needs someone comfortable doing either. You'll join a multidisciplinary environment alongside ML scientists, ML engineers, and computational biologists. The ideal candidate combines strong software and ML engineering skills, a systems mindset, fluency in how agentic systems are actually built and evaluated, and a "get-it-done" attitude.

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)

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

Benefits

Discretionary Annual Bonus
Relocation Not Available

Company overview

Roche is a Swiss multinational healthcare company and a global pioneer in pharmaceuticals and diagnostics, founded in 1896 and headquartered in Basel, Switzerland. As the world's largest biotech company and the fifth-largest pharmaceutical company by revenue, Roche operates through two primary divisions: Pharmaceuticals and Diagnostics. The company is renowned for its leadership in personalized healthcare, combining strengths in both drug development and diagnostic testing to deliver tailored treatments for patients. Roche maintains a strong focus on oncology, immunology, infectious diseases, ophthalmology, and central nervous system disorders, with a portfolio that includes over 30 medicines on the World Health Organization's Model Lists of Essential Medicines. The company owns significant subsidiaries including Genentech, Chugai Pharmaceuticals, Ventana Medical Systems, and Foundation Medicine. With approximately 103,000 employees worldwide and operations in over 100 countries, Roche has established itself as a leader in cancer treatments globally while maintaining a robust pipeline of innovative therapies and diagnostic solutions. The company's commitment to sustainability has been recognized through ten consecutive years as Group Leader in the Dow Jones Sustainability Indices within the Pharmaceuticals, Biotechnology & Life Sciences Industry.