Machine Learning Scientist II, Drug Discovery Analytics

Role: Machine Learning Scientist II supporting drug discovery at Revolution Medicines, focusing on predictive modeling and advanced analytics to accelerate development of RAS-targeted oncology therapies. Responsibilities: develop, validate, and apply machine learning models for target discovery, compound optimization, and phenotypic screening; integrate heterogeneous datasets; collaborate with cross-functional teams including chemists and biologists; ensure reproducibility and clear communication of methods and findings. Requirements: Ph.D. in a quantitative field (e.g., computational biology, chemistry, computer science), 6-8 years of relevant industry experience with proven model development skills; strong Python expertise with scientific libraries; experience with ML frameworks such as PyTorch or TensorFlow; data analysis skills for noisy biological and chemical datasets. Preferred: biotech or pharma experience; familiarity with phenotypic screening, high-content imaging, cheminformatics tools like RDKit; knowledge of multi-omics, cloud computing, MLOps, and biological interpretation of ML results. High-Value: focus on oncology, biomarkers, phenotypic data, AI-driven target and compound discovery, with a collaborative, research-focused environment. Work Setup: based in Redwood City, CA; salary range $202K-$238K, with comprehensive benefits.