ML Scientist II - Drug Discovery Analytics

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

Job summary

Revolution Medicines is seeking a Machine Learning Scientist II to accelerate drug discovery by building predictive models and applying AI to chemical and biological data. You will collaborate at the interface of data science, chemistry, and biology to translate scientific questions into computational analyses for research teams.

The role involves developing ML models for compound activity, selectivity, and drug developability, working with medicinal chemists and researchers to deliver insights,

Qualifications

• PhD or MS in a quantitative field with research experience in ML applied to biology or chemistry.
• 2-5 years of applying ML/data science to scientific datasets.
• Strong Python programming and experience with ML frameworks.
• Experience with data visualization and exploratory data analysis.
• Ability to communicate complex results to cross-functional partners.

Responsibilities

• Develop and implement ML models to support drug discovery questions (activity, selectivity, developability).
• Collaborate with data science, chemistry, and biology teams to translate questions into analyses.
• Prepare and evaluate predictive models; ensure robust validation and reproducibility.
• Produce reusable workflows and contribute to discovery pipelines.
• Document methods, code, and results for knowledge sharing.

Skills

Python

NumPy

Pandas

SciPy

PyTorch

TensorFlow

scikit-learn

Education

PhD in machine learning or related field

MS degree with relevant industry experience

Tools

RDKit

OpenEye

Job description

Revolution Medicines is seeking a Machine Learning Scientist II to accelerate drug discovery by building predictive models and applying AI to chemical and biological data. You will collaborate at the interface of data science, chemistry, and biology to translate scientific questions into computational analyses for research teams.

The role involves developing ML models for compound activity, selectivity, and drug developability, working with medicinal chemists and researchers to deliver insights,