Principal Machine Learning Scientist - Drug Discovery Analytics

3w3 weeks ago

Revolution Medicines

Redwood City, US · Full-time · $273,000 – $321,000

About this role

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The R&D pipeline comprises RAS(ON) inhibitors like daraxonrasib (RMC-6236), elironrasib (RMC-6291), zoldonrasib (RMC-9805), and RMC-5127 in clinical development. Join outstanding professionals committed to patients with RAS signaling pathway mutations.

The Principal Machine Learning Scientist leads development of advanced machine learning approaches to accelerate small-molecule drug discovery. This role sits at the intersection of data science, chemistry, and biology, transforming complex datasets into predictive models for target discovery and compound design. Insights guide translational hypotheses for RAS-driven cancers.

Working closely with experimental scientists, develop cutting-edge modeling that integrates chemical, biological, and phenotypic data. Design predictive models and deploy innovative algorithms to advance data-driven discovery strategies. Translate insights into actionable decisions that improve speed and success of medicine discovery.

Partner with medicinal chemists, biologists, data scientists, and ML engineers in multidisciplinary teams. Drive adoption of innovative modeling approaches across discovery workflows. Play a key role in scientific leadership by identifying AI opportunities for better decision-making.

Requirements

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)
Expertise in data analysis and scientific computing
Experience developing models for chemical and biological data integration
Proficiency in modern ML techniques including graph neural networks and generative models

Responsibilities

Define and lead machine learning strategies that accelerate early-stage drug discovery
Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making
Drive the adoption of innovative modeling approaches within multidisciplinary discovery teams
Develop predictive models for compound activity, selectivity, ADME/Tox, and developability properties
Develop predictive models for target engagement, mechanism-of-action, and phenotypic datasets
Apply modern ML techniques such as graph neural networks, deep learning for molecular representation, generative chemistry models, and active learning frameworks
Partner with medicinal chemists to guide compound design and optimization
Integrate heterogeneous datasets including chemical structure, screening data, imaging, phenotypic screening, structural biology, and molecular simulation outputs