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

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. ​

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 syste