Machine Learning Scientist II

Role overview This hands-on individual contributor role supports a clinical-stage oncology company dedicated to developing innovative medicines for patients with RAS-addicted cancers. The position applies advanced analytics and machine learning to accelerate drug discovery across target identification, compound optimization, phenotypic screening, and translational research, sitting at the interface of data science, chemistry, and biology.

Responsibilities - Develop, implement, and evaluate machine learning models that address 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 data, including chemical structures, screening outputs, structural biology data, molecular simulation outputs, and high-content imaging or morphological profiling data - Apply supervised learning, deep learning, graph-based methods, and ensemble approaches under the guidance of project and functional leads, using sound validation strategies to assess model performance and limitations - Collaborate with data engineering and machine learning engineering partners to support reproducible workflows and integrate analytical outputs into discovery pipelines - Partner with medicinal chemists, biologists, and other research scientists to translate scientific questions into computational analyses and document methods, code, results, and assumptions for reproducibility

Requirements - PhD in machine learning, computational biology, computational chemistry, computer science, statistics, bioinformatics, or a related quantitative field, or an MS degree with relevant industry experience - Typically two to five years of relevant experience applying machine learning, data science, or advanced analytics to scientific datasets, with relevant doctoral research considered toward this expectation - 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

Nice to have - Background in biotechnology, pharmaceutical, healthcare, or drug discovery environments - Experience with phenotypic or high-content imaging 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 and work setup The position is hybrid, based out of the Redwood City, California headquarters. The posted base pay salary range is $182,000–$214,000 USD, with the actual offer determined by role, level, location, skills, experience, and market dynamics. Total rewards include competitive cash compensation, equity awards, benefits, and learning and development opportunities.