Senior Machine Learning Scientist, Drug Discovery Analytics

Revolution Medicines•Redwood City, CA

15h•Hybrid

About The Position

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. We are seeking a Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions. The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems. The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.

Requirements

PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.
4–8 years experience applying machine learning or advanced analytics to scientific datasets.
Python and scientific computing libraries (NumPy, Pandas, SciPy).
Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).
Model development, validation, and evaluation methods.
Data visualization and exploratory analysis.
Experience working with noisy and incomplete experimental datasets.

Nice To Haves

Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).
Multi-omics data analysis.
Cloud computing environments.
MLOps or scalable model deployment.

Responsibilities

Develop Predictive Models for Drug Discovery
Design and implement machine learning models to predict compound activity, selectivity, and developability.
Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.
Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.
Evaluate model performance and apply appropriate validation strategies.
Work with data engineers and ML engineers to integrate models into discovery pipelines.
Analyze Complex Scientific Data
Perform exploratory data analysis on chemical, biological, and phenotypic datasets.
Integrate heterogeneous datasets including: Chemical structure and screening data. High-content imaging data. Structural biology and molecular simulation outputs.
Identify patterns and relationships that inform scientific hypotheses.
Collaborate with Research Scientists
Partner with medicinal chemists to support compound design and lead optimization.
Work with biologists to interpret experimental results and identify new target opportunities.
Translate scientific questions into computational modeling strategies.