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. The company’s RAS(ON) inhibitors 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, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway. The Opportunity: We are seeking a Director 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 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 the discovery of medicines for patients with RAS-driven cancers. Key responsibilities include: Scientific Leadership: Provide hands-on scientific leadership in drug discovery analytics spanning Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making Managing, coaching and mentoring scientists across the function in order to develop their skills and build RevMed’s 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 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 and engineers and ML engineers to deploy models into scalable discovery workflows. Required Skills, Experience and Education: PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline. 8+ years experience applying machine learning or advanced analytics to scientific problems. Demonstrated experience working with chemical or biological datasets in drug discovery or related domains. 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 both individuals and teams in order to build long-term organizational capability Passion for scientific innovation and a relentless 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 is highly desirable. Familiarity with cloud computing and scalable ML workflows is a plus Ability to work at the interface of computational and experimental science. #LI-Hybrid #LI-LN1 The base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. The range displayed on each job posting is intended to be the base pay salary range fo…