ML Scientist II, Drug Discovery Analytics

Revolution Medicines, Inc.; San Francisco (CA); On-site; USD 202,000 - 238,000; Full time

Job summary

Revolution Medicines, Inc. in Redwood City, CA is seeking a Machine Learning Scientist II to accelerate drug discovery by building predictive models from chemical, biological, and imaging data.

You will collaborate with chemists and biologists, develop reusable workflows, and apply supervised and deep learning methods to questions about activity, selectivity and developability. Strong Python and ML framework skills are required; a PhD or MS with industry experience is welcomed.

Qualifications

• PhD in ML, computational biology, computational chemistry, CS, statistics, bioinformatics, or related field; or MS with relevant industry experience.
• Typically 6-8 years applying ML, data science, or analytics to scientific datasets.
• Demonstrated experience developing, validating, and evaluating predictive or classification models.
• Strong Python programming with NumPy, Pandas, SciPy.
• Hands-on experience with PyTorch, TensorFlow, and/or scikit-learn.
• Ability to communicate technical work clearly and collaborate with cross-functional partners.

Responsibilities

• Develop, validate, and deploy ML models for drug discovery questions (activity, selectivity, developability).
• Perform exploratory data analysis on chemical, biological, imaging, and phenotypic data.
• Integrate heterogeneous datasets (chemical structures, screening, biology outputs) for modeling.
• Apply supervised learning, deep learning, graph-based, and ensemble methods under guidance.
• Collaborate with data engineering to ensure reproducible workflows and integration into discovery pipelines.

Skills

Machine learning

Python

PyTorch

Data analysis

Collaboration

NumPy/Pandas

Education

Ph.D. in ML / related field

MS with relevant industry experience

Tools

RDKit

OpenEye

Jupyter

Job description

Revolution Medicines, Inc. in Redwood City, CA is seeking a Machine Learning Scientist II to accelerate drug discovery by building predictive models from chemical, biological, and imaging data.

You will collaborate with chemists and biologists, develop reusable workflows, and apply supervised and deep learning methods to questions about activity, selectivity and developability. Strong Python and ML framework skills are required; a PhD or MS with industry experience is welcomed.