Vice President, Global Head of AI Drug Discovery; AIDD

Position: Vice President, Global Head of AI for Drug Discovery (AIDD)
The Position The Vice President and Global Head of AI for Drug Discovery (AIDD) is responsible for leading a world-class artificial intelligence and machine learning department focused on designing therapeutics across the entire Roche/Genentech Research and Early Development (RED) portfolio. As a member of the Computational Sciences Center of Excellence (CSCoE) leadership team, you will also play a critical role in advancing the development and deployment of Artificial Intelligence (AI) and other computational methods across Research and Early Development at Roche the REDs.

Key responsibilities include building, scaling, and deploying cutting-edge AI methodologies, models, and computational frameworks for antibody design, protein engineering, and small molecule design. Additionally, the Global Head will partner closely with Discovery, Early Development, Therapeutic Area leads, and Translational teams to ensure the methods developed can be applied across the Roche/Genentech portfolio. The Global Head will display outstanding leadership skills—building and mentoring an elite team of AI scientists, machine learning engineers, and computational researchers into a cohesive, high-impact organization, while forging strategic partnerships across Genentech Research and Early Development (gRED), Roche Pharma Research and Early Development (pRED), and the external AI community.

Key Responsibilities Scientific Leadership:

Define and execute an AI-driven drug discovery strategy, roadmap, and long-term vision to enable rapid progression in therapeutic design, balancing technology development and portfolio impact to create a highly effective, collaborative drug discovery engine.

Set the strategy for Roche/Genentech’s pioneering Lab-in-the-Loop with partners across the REDs to support portfolio decision milestones especially hit identification and lead optimization. Drive measurable portfolio impact by embedding AI into discovery programs across gRED and pRED, in partnership with therapeutic area and discovery leaders.

Champion hypothesis-driven drug discovery by integrating predictive computational methods, antibody design, protein engineering and  medicinal chemistry.

Track the external state of the art, including in co-folding and generative models and decide where to build, adopt or partner.

Build an environment where state-of-the-art AI research can flourish, managing a balanced portfolio of AI investments, spanning portfolio-tied methods and frontier research, with distinct resourcing, success criteria, and time horizons for each, while protecting space for blue-sky work that can redefine how Roche/Genentech discovers medicines.

Strategic & Organizational Leadership:

Build and lead the global AIDD organization, including AI/ML scientists and engineers  and the managers who lead them. Hire, mentor, and hold a high bar.

Pioneer a strategic shift of drug discovery across Roche/Genentech by ensuring that state-of-the-art computational models are used to accelerate the design and optimization of antibodies, proteins and small molecules, thereby increasing the number of effective medicines in the Roche/Genentech portfolio.

Set the overall strategy and make critical decisions regarding talent, infrastructure, resourcing, and capability development for the AIDD organization.

As part of the leadership team of the CSCoE, contribute to the strategic direction of all computational research across Research and Early Development at Roche/Genentech.

Run the AIDD organization as a disciplined, global operation: own the budget and resource allocation across sites, design an org structure that matches the scientific strategy, establish a clear leadership operating rhythm with the AIDD leadership team Drive sustained investment in engineering and platform capabilities that underpin all research and portfolio work.

AI Innovation & Computational Excellence:

Establish AIDD as the central engine for applying generative AI, machine learning, and predictive modeling to structural biology, molecular design, and lead optimization.

Translate state-of-the-art AI advancements into tangible drug discovery outcomes by building, scaling, and deploying proprietary foundation models, deep learning architectures, and automated design loops into core discovery programs.

Set the foundation model strategy for Roche/Genentech drug discovery: decide where proprietary models create durable differentiation versus where open models suffice, and build shared foundation model…