Senior Machine Learning Scientist I, Drug Discovery Analytics

About Revolution Medicines

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company's R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company's RAS(ON) inhibitors include daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, which are currently in clinical development.

The Opportunity

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.

Key Responsibilities

Develop Predictive Models for Drug Discovery

• Independently design and implement machine learning models to predict compound activity, selectivity, and developability
• Identify and 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, and structural biology and molecular simulation outputs

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

Required Skills, Experience and Education

• PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field
• 6–10 years of 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

Preferred Skills

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

Compensation and Benefits

Base Pay Salary Range

$229,000—$269,000 USD

Base pay salary range is one part of the overall total rewards program at Revolution Medicines, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities. The range displayed is for candidates working onsite at headquarters in Redwood City, CA and will be adjusted for the local market a candidate is based in. Individual base pay salary is determined by multiple factors, including job-related skills, experience, market dynamics, and relevant education or training.

Employment

Revolution Medicines is an equal opportunity employer and prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital status, medical condition, and veteran status.

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