Machine Learning Scientist II

Summary

Revolution Medicines is an oncology company developing RAS(ON) inhibitors for patients with RAS-addicted cancers. The Machine Learning Scientist II will develop and apply predictive models and analytical methods to chemical and biological datasets, supporting target discovery, compound optimization, phenotypic screening, and translational research. The role collaborates with data, engineering, chemistry, and biology teams to create reproducible computational workflows and communicate scientific insights.

Responsibilities

• Develop, implement, and evaluate machine-learning models that support drug discovery questions, including compound activity, selectivity, developability, target engagement, and phenotypic screening outcomes
• Perform exploratory data analysis and quality assessment on chemical, biological, imaging, and phenotypic datasets
• Prepare and integrate heterogeneous datasets, including chemical structure and screening data, structural biology outputs, molecular simulation outputs, and high-content imaging or morphological profiling data
• Apply appropriate modeling approaches, including supervised learning, deep learning, graph-based methods, and ensemble methods, under the guidance of project and functional leads
• Use sound validation strategies to assess model performance, robustness, applicability, and limitations
• Collaborate with data engineering and machine-learning engineering partners to support reproducible workflows and integration of analytical outputs into discovery pipelines
• Partner with medicinal chemists, biologists, and other research scientists to translate scientific questions into computational analyses and communicate results clearly
• Document methods, code, results, and key assumptions in a manner that supports reproducibility and knowledge sharing

Skills

• Ph.D. in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field; or a M.S. degree with relevant industry experience
• Typically 2-5 years of relevant experience applying machine learning, data science, or advanced analytics to scientific datasets; relevant doctoral research may be considered
• Demonstrated experience developing, validating, and evaluating predictive or classification models
• Strong Python programming skills and experience with scientific computing libraries such as NumPy, Pandas, and SciPy
• Hands-on familiarity with machine-learning frameworks such as PyTorch, TensorFlow, and/or scikit-learn
• Experience with data visualization, exploratory data analysis, and working with noisy or incomplete experimental datasets
• Ability to communicate technical work clearly and collaborate effectively with cross-functional scientific partners
• Experience in biotechnology, pharmaceutical, healthcare, or drug discovery environments
• Experience with phenotypic screening, high-content imaging, Cell Painting, morphological profiling, or computer vision for microscopy images
• Familiarity with representation learning, self-supervised learning, embedding generation, dimensionality reduction, clustering, or phenotype discovery
• Familiarity with cheminformatics or molecular modeling tools, such as RDKit or OpenEye
• Experience with multi-omics data analysis, cloud computing environments, MLOps, or scalable model deployment
• Working knowledge of cell biology, drug discovery workflows, assay development, microscopy, experimental design, or biological interpretation of machine-learning results

Qualifications

Must Haves

• Ph.D. in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field; or a M.S. degree with relevant industry experience
• Typically 2-5 years of relevant experience applying machine learning, data science, or advanced analytics to scientific datasets; relevant doctoral research may be considered
• Demonstrated experience developing, validating, and evaluating predictive or classification models
• Strong Python programming skills and experience with scientific computing libraries such as NumPy, Pandas, and SciPy
• Hands-on familiarity with machine-learning frameworks such as PyTorch, TensorFlow, and/or scikit-learn
• Experience with data visualization, exploratory data analysis, and working with noisy or incomplete experimental datasets
• Ability to communicate technical work clearly and collaborate effectively with cross-functional scientific partners

Nice to Haves

• Experience in biotechnology, pharmaceutical, healthcare, or drug discovery environments
• Experience with phenotypic screening, high-content imaging, Cell Painting, morphological profiling, or computer vision for microscopy images
• Familiarity with representation learning, self-supervised learning, embedding generation, dimensionality reduction, clustering, or phenotype discovery
• Familiarity with cheminformatics or molecular modeling tools, such as RDKit or OpenEye
• Experience with multi-omics data analysis, cloud computing environments, MLOps, or scalable model deployment
• Working knowledge of cell biology, drug discovery workflows, assay development, microscopy, experimental design, or biological interpretation of machine-learning results

Benefits

• Robust equity awards
• Significant learning and development opportunities
• Hybrid work arrangement