Executive Director, Digital Drug Discovery and AI Strategy

Executive Director, Digital Drug Discovery and AI Strategy

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins, and RAS companion inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team, you will join other outstanding Revolutionaries in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

As Executive Director, AI/ML Research & Scientific Computing, you will be a senior leader shaping the future of data-driven drug discovery at Revolution Medicines. You will define the Research’s AI/ML strategy, oversee implementation of advanced analytics across discovery, and build a world-class team to unlock the full potential of our rich internal datasets and make these insights accessible to other scientists.

Your leadership will directly influence early discovery innovation, pipeline acceleration, and strategic portfolio decisions.

• Technical Leadership and Innovation: You will have a deep understanding of Machine Learning Operations and AI system design. Design and scale a robust ML infrastructure, integrating MLOps best practices to ensure seamless development, deployment, and monitoring of AI/ML models. Drive innovation in applying deep learning, generative models, and diffusion models to drug discovery, including target prediction, hit identification, and lead optimization.
  
  Leverage Rev Med’s unique datasets to generate novel hypotheses and enable data-driven decision-making across the research continuum.
• Strategic Vision and Leadership: Define the long-term vision for AI/ML-enabled discovery and align it with organizational priorities. Build, mentor, and inspire a cross-disciplinary team of data scientists, computational chemists, engineers, and biologists. Establish key performance metrics and ensure delivery of impactful solutions that accelerate portfolio progression.
• External and
  
  Cross-Functional Collaboration:
  
  Forge partnerships with leading academic groups, technology innovators, and compute providers to access cutting-edge platforms and methodologies. Collaborate across research, chemistry, biology, and IT to identify high-impact opportunities for AI/ML integration and ensure smooth deployment of scalable solutions. Act as a thought leader internally and externally, representing Rev Med at scientific forums and shaping the broader field of AI-driven drug discovery.

Ph.D. or master’s degree in Computer Science, Machine Learning, Physics, Mathematics or a relevant scientific discipline with over 15+ years’ experience, exposure to drug discovery is a plus.

• Expertise in a wide variety of AI/ML-based computational techniques and in developing adaptable ML workflows to solve challenging problems.
• Deep understanding of AI/ML techniques such as deep learning, reinforcement learning, and generative models.
• Expertise in frameworks such as PyTorch and Tensor Flow, training, and fine-tuning models on GPUs.
• Track record of deploying AI/ML solutions at scale.
• Proven track record of leadership and cross-functional collaboration across ML scientists, software engineering, and MLOps.
• Previous experience leading large AI/ML projects that require engaging with collaborators across the board with varying degrees of expertise.
• Expertise in machine learning infrastructure and MLOps, including cloud and on-prem compute environments.
• Demonstrated ability to build, scale, and lead high-performing cross-functional teams.
• Strong communication and leadership skills with the ability to bridge diverse scientific and technical disciplines.
• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.
• Knowledge of oncology…