Principal Machine Learning Engineer, Foundation Models, AI for Drug Discovery
AI summary of the role
Architect and deploy autonomous agents for multi-step reasoning across drug discovery workflows, including agent memory and context management.
What you’ll do
- Design and optimize large-scale distributed training and inference systems for foundation models; own production Python/PyTorch codebases.
- Establish MLOps/AgentOps best practices: experiment tracking, observability, evaluation harnesses, CI/CD, and infrastructure.
- Define long-term engineering roadmap for agentic and foundation models; serve as technical authority for ML infrastructure.
- Partner with ML Scientists to translate open-ended scientific problems into scoped, shippable systems.
What you’ll bring
- BS/MS/PhD in CS, ML, Engineering or related field with 10+/8+/5+ years of industry experience for BS/MS/PhD respectively.
- Exceptional Python programming and rigorous software engineering fundamentals (Git, automated testing, CI/CD).
- Hands-on experience with PyTorch or JAX and deploying ML infrastructure on AWS or HPC with distributed training.
- Practical experience with agent orchestration frameworks (e.g., LangGraph, MCP) and persistent agent memory.
Technologies
Python · PyTorch · JAX · AWS · HPC · LangGraph · MCP · LLM · MLOps · AgentOps · CI/CD
About Genentech
Biotechnology company discovering and developing medicines for serious and life-threatening diseases (oncology, neuroscience, immunology); operates as Roche's independent US pharma R&D and commercial arm.
Acquired · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. The Opportunity: 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. In this role, you will drive the engineering, scaling, and operationalization of our internal reasoning Large Language Models (LLMs) and agentic systems, enabling them to succeed at complex biomolecular design and autonomous scientific workflows. You will work at the intersection of engineering and research, spanning the full stack: from the agent orchestration logic that makes these systems scientifically useful, to the distributed infrastructure and MLOps/AgentOps that make them robust at scale. In this role, you will: Agentic
More from the job description
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. The Opportunity: 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. In this role, you will drive the engineering, scaling, and operationalization of our internal reasoning Large Language Models (LLMs) and agentic [... source excerpt omitted ...] m the agent orchestration logic that makes these systems scientifically useful, to the distributed infrastructure and MLOps/AgentOps that make them robust at scale. In this role, you will: 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 [... source excerpt omitted ...] dation 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. Who You Are: Education & Experience: 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
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