Engineering Team Lead - AI for Drug Discovery

Roche • South San Francisco, CA

8d

About 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 Opportunity Genentech and Roche have long defined the forefront of modern drug discovery. Today, we are amplifying that legacy through a revolutionary transformation powered by AI and machine learning. Our "lab in the loop" strategy couples our world-class experimental capabilities with massive quantities of data to train AI models that accelerate the discovery of new medicines. To enable this vision, we are seeking an exceptional Engineering Manager to lead the Therapeutic Molecule Registration (TMR) team. TMR is the "central nervous system" for managing molecular data across our global research organization. This role sits at the intersection of infrastructure modernization and AI innovation. You will lead the development of the next-generation system that will not only underpin our new AI-driven discovery workflows but replace the legacy registration systems currently used in our labs. You are building the foundational backbone for all drug discovery infrastructure at Genentech. The prototyping for this platform is currently in progress, and a dedicated engineering team is in place. We need a leader to guide this group through the critical transition from prototype to a global production standard—retiring legacy tools and deploying a unified, high-performance system that supports everything from daily wet-lab operations to massive-scale computational design. You will lead the TMR engineering team, a core unit within the AI for Drug Discovery (AIDD) organization. As part of the broader Computational Sciences Center of Excellence (CS-CoE), our team combines the agility of a tech startup with the resources and impact of a world-class biotech. You will partner directly with machine learning researchers and wet-lab scientists to translate ambitious scientific strategies into production software.

Requirements

• 12+ years of software engineering experience, with at least 3+ years specifically managing engineering teams (direct people management, not just technical lead).
• Experience taking systems from prototype to production, specifically in complex data environments.
• Expertise in scalable data systems (handling 100B+ rows).
• Strong technical proficiency in Python and relational databases (Postgres preferred).
• Excellent communication skills, with the ability to bridge the gap between technical engineering constraints and scientific business goals.

Nice To Haves

• Open source cheminformatics experience (e.g., RDKit, chemfp, Indigo, HELM toolkit).
• Chemical database cartridge expertise.
• Familiarity with biological sequence alignment or chemical structure canonicalization.

Responsibilities

• Lead and mentor an existing team of software engineers, providing code coaching, performance management, and career development to foster a culture of engineering excellence.
• Take ownership of the current TMR platform prototype, driving the architectural roadmap to evolve it into a robust, cloud-native production system capable of handling 100M+ molecules.
• Make high-level technical decisions regarding the stack, ensuring solutions are scalable, maintainable, and cost-effective.
• Establish engineering best practices (CI/CD, testing standards, code reviews) to ensure high reliability as the platform scales to support global research.
• Collaborate closely with Genentech Computational Sciences (gCS) and Antibody Engineering teams to translate complex scientific requirements into clear engineering deliverables.
• Manage project timelines and priorities, ensuring the team delivers high-impact features that unblock drug discovery programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees