Machine Learning Scientist II at Revolution Medicines

Job description

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 RAS. Founded 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 rigor—all 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. #LI-Hybrid #LI-SB1 The base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. The range displayed on each job posting is intended to be the base pay salary range for an individual working onsite in Redwood City and will be adjusted for the local market a candidate is based in. Our base pay salary ranges are determined by role, level, and location. Individual base pay salary is determined by multiple factors, including job-related skills, experience, market dynamics, and relevant education or training. Please note that base pay salary range is one part of the overall total rewards program at RevMed, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities. 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. Revolution Medicines takes protection and security of personal data very seriously and respects your right to privacy while using our website and when contacting us by email or phone. We will only collect, process and use any personal data that you provide to us in accordance with our CCPA Notice and Privacy Policy . For additional information, please contact privacy@revmed.com . Base Pay Salary Range $182,000 — $214,000 USD We are aware of recent recruitment scams in which individuals or organizations falsely represent themselves as being affiliated with Revolution Medicines. These scams may appear as false job advertisements or unsolicited contacts through communication or chat platforms, email, phone, or text message. Please note that Revolution Medicines does not extend unsolicited employment offers and will never ask candidates to provide financial information, purchase equipment, or pay fees as part of the hiring process. All legitimate communication from Revolution Medicines will come from an official @revmed.com email address. If you believe you’ve been contacted by someone impersonating a Revolution Medicines recruiter, please report it to careers@revmed.com so we can share these impersonations with our IT team for tracking and awareness.

Requirements

Must-haves

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 rigor—all in service of patients. The Opportunity:
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.
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.

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.
#LI-Hybrid #LI-SB1

Application prep

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Keywords to mirror

machine learningpython

Platform: greenhouse
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Benefits

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