Senior Machine Learning Engineer - Foundational ML, AI for Biology & Translation (AIBT

The Position

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 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 Senior ML Engineer to join our Foundation Models team and help build and scale the next generation of foundation models and agentic systems for therapeutic discovery.

The successful candidate will contribute to the design, development, and scaling of the next generation of large-scale foundation models and AI agents, with the ultimate aim of accelerating target and drug discovery. In this role, the candidate will build, product ionize, and operate these systems, with a strong emphasis on the infrastructure, Agent Ops, and MLOps that make them robust, reproducible, and efficient  candidate will join an exciting, multidisciplinary research environment alongside ML scientists, ML engineers, and computational biologists.

The ideal candidate will combine strong software and ML engineering skills, a systems mindset, and a "get-it-done" attitude. The selected candidate will be a technical leader, driving the delivery of significant technical solutions, raising engineering quality across projects, and partnering closely with researchers to move ideas into impactful applications.

• Build, scale, and product ionize foundation models and AI agents that support target discovery, experimental design, and lab-in-the-loop pipelines.

• Design and operate the Agent Ops and MLOps backbone for these systems, including experiment tracking, model and agent evaluation, monitoring, and reproducible training and inference workflows.

• Design, implement, and maintain scalable and reliable ML infrastructure on AWS, and optimize distributed training and inference on high-performance compute.

• Manage and optimize CI/CD pipelines and Git repositories for ML projects, ensuring efficient version control to support collaboration and deployment.

• Automate deployment, monitoring, and operational tasks using infrastructure-as-code and orchestration tooling (e.g., Terraform, Helm, Kubernetes).

• Proactively identify issues and gaps, propose improvements, and champion engineering best practices and code quality across the team.

• Collaborate closely with interdisciplinary and cross-functional teams across gRED and Roche, and help support research output where relevant, including publications.

• Educational background: BS/MS in Computer Science, Machine Learning, Engineering, or a related quantitative field.

• Experience:
  
  5+ years of industry experience building and delivering ML systems.

• Technical skills:

• Excellent Python programming skills, and proficiency in scripting languages for automation.

• Solid working knowledge of the theory and practice of deep learning, and hands‑on experience with ML frameworks such as PyTorch or JAX.

• Practical experience building, fine tuning, deploying, and scaling foundation models, LLMs, and/or…