Machine Learning Scientist II

Revolution Medicines is a global commercial-state oncology company dedicated to discovering developing and delivering innovative medicines for patients with RAS-addicted cancers. Leveraging its differentiated RAS(ON) tri-complex inhibitor platform the company is advancing a broad integrated portfolio of oral RAS(ON) inhibitors designed to directly target the active cancer-driving state of on rigorous scientific inquiry and a willingness to challenge long-held assumptions Revolution Medicines is committed to changing the trajectory of disease for patients with RAS-addicted cancers worldwide.

Our people are united by a shared way of working: follow the science challenge assumptions act with urgency and hold ourselves to a high standard of rigorall in service of patients.

The Opportunity:

• We are seeking a Machine Learning Scientist II to help accelerate drug discovery through advanced analytics and artificial intelligence. This hands-on individual contributor will develop and apply predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights for research teams.

• The Machine Learning Scientist II will work at the interface of data science chemistry and biology to support target discovery compound optimization phenotypic screening and translational research. The role is well suited to a scientist who brings strong technical foundations in machine learning curiosity about drug discovery and a collaborative approach to solving real-world scientific problems.

• Working with senior data scientists and experimental collaborators the successful candidate will contribute analyses models and reusable workflows to a data-driven discovery ecosystem where data analytics and experimentation continuously inform one another.

• 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.

Required Skills Experience and Education:

• 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 6-8 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.

Preferred Skills:

• 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.

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