Principal Machine Learning Engineer, Foundation Models, AI Drug Discovery

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 organizations 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 maximizing 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.

At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning. We are seeking a Principal Machine Learning Engineer to join our Foundation Models team.

• Own the MLOps/Agent Ops Stack for Foundation Models:
  Establish rigorous evaluation harnesses that check agent output against scientific ground truth, and the experiment tracking, system observability and monitoring, CI/CD, and infrastructure those systems depend on.
• Agentic Systems Engineering:
  Architect and deploy autonomous agents that utilize tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows. Design and implement advanced agent memory architectures and context management for long-horizon scientific tasks. Build reliable interfaces between agents and genomic, chemical, and clinical data sources.
• Scalable ML Systems & Productionization:
  Design, build, and optimize large-scale distributed training and inference systems for foundation models. Own the production Python/PyTorch codebases that turn fast-moving research into enterprise-grade software.
• Technical Strategy & Leadership:
  Define the long-term engineering roadmap for AI4DD’s agentic and foundation models. Serve as a technical authority on ML infra for Genentech leadership, architect cross-functional platforms, and elevate the engineering bar across gRED.
• Research-to-Production Translation:
  Partner closely with ML Scientists and domain experts to translate open-ended scientific problems and complex reasoning objectives into scoped, shippable, and highly efficient systems.

BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field. You have a demonstrated track record of technical leadership with increasing levels of experience based on degree:
PhD with 5+ years, MS with 8+ years, or BS with 10+ years of industry experience building, shipping, and owning large-scale ML systems and infrastructure end-to-end.

Exceptional Python programming skills and rigorous software engineering fundamentals (Git, automated testing, CI/CD, documentation, architecture design).

Extensive hands-on experience with modern deep learning frameworks (PyTorch, JAX) and deploying ML infrastructure on AWS or HPC environments, including distributed training tools.

Practical experience designing agent orchestration frameworks (e.g., Lang Graph, MCP-based tool integration), managing persistent agent memory, and building self-improving loops.

Strong passion for applying frontier AI and agentic science to AI for Drug Discovery…