Director Machine Learning, Drug Discovery Analytics

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins, including daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, currently in clinical development.

About the Role

We are seeking a Director of Machine Learning to lead the development of advanced machine learning approaches that accelerate small-molecule drug discovery. This role sits at the intersection of data science, chemistry, and biology, transforming complex scientific datasets into predictive models that guide target discovery, compound design, and translational hypotheses. Working closely with experimental scientists, the Director of ML will develop cutting-edge modeling approaches that integrate chemical, biological, and phenotypic data with their team. The successful candidate will play a key role in advancing a data-driven discovery strategy by designing predictive models, deploying innovative algorithms, and translating insights into actionable decisions that improve the speed and success of discovering medicines for patients with RAS-driven cancers.

Responsibilities

• Scientific Leadership:
  • Provide hands-on scientific leadership in drug discovery analytics.
  • Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making.
  • Manage, coach, and mentor scientists to develop their skills and build organizational capabilities.
  • Define and lead machine learning strategies that accelerate early-stage drug discovery.
• Model Development:
  • Develop predictive models for compound activity, selectivity, ADME/Tox, and developability properties.
  • Create models for target engagement, mechanism-of-action, and phenotypic datasets.
• Cross-Functional Collaboration:
  • Work with biologists to interpret complex experimental datasets and generate mechanistic hypotheses.
  • Collaborate with data scientists, engineers, and ML engineers to deploy models into scalable discovery workflows.

Requirements

• PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline.
• 8+ years of experience applying machine learning or advanced analytics to scientific problems.
• Demonstrated experience working with chemical or biological datasets in drug discovery or related domains.

Skills

• Strong expertise in Python-based ML ecosystems (PyTorch, TensorFlow, scikit-learn).
• Data analysis and scientific computing (NumPy, Pandas).
• Deep learning and representation learning techniques.
• Evidence of successful coaching, mentorship, and development of individuals and teams.
• Passion for scientific innovation and a commitment to improving patient outcomes.

Preferred Skills

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.
• Experience with cheminformatics or bioinformatics toolkits.
• Familiarity with cloud computing and scalable ML workflows.
• Ability to work at the interface of computational and experimental science.

Pay

Base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA: $273,000 - $321,000 USD. The range will be adjusted for the local market where a candidate is based. Individual base pay salary is determined by multiple factors, including job-related skills, experience, and market dynamics.

Benefits

Total rewards program at Revolution Medicines includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities.