Senior Machine Learning Scientist - Drug Discovery Analytics

3w3 weeks ago

Revolution Medicines

Redwood City, US · Full-time · $229,000 – $269,000

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 including daraxonrasib (RMC-6236), elironrasib (RMC-6291), zoldonrasib (RMC-9805), and RMC-5127 in clinical development. Join outstanding professionals in a tireless commitment to patients with cancers harboring RAS pathway mutations.

We seek a Senior Machine Learning Scientist to accelerate drug discovery through advanced analytics and artificial intelligence. This role develops predictive models and analytical methods transforming complex biological and chemical datasets into actionable insights guiding research decisions. Work at the interface of data science, chemistry, and biology.

Support target discovery, compound optimization, and translational research by designing machine learning models for compound activity, selectivity, developability, ADME/Tox, and phenotypic outcomes. Perform exploratory data analysis on chemical, biological, phenotypic datasets including high-content imaging and structural biology outputs. Identify patterns and relationships informing scientific hypotheses.

Collaborate with medicinal chemists for compound design and lead optimization, and with biologists to interpret results and identify targets. Contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another. Partner effectively with experimental scientists to solve real-world problems.

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

Responsibilities

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
Perform exploratory data analysis on chemical, biological, and phenotypic datasets
Integrate heterogeneous datasets including chemical structure, screening data, high-content imaging, structural biology, and molecular simulation outputs
Partner with medicinal chemists to support compound design and lead optimization