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Senior ML scientist builds predictive models and analytics to accelerate drug discovery for RAS-addicted cancers, integrating biological, chemical, and structural datasets using deep learning and cheminformatics tools.
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 Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions.
The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems.
The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.
Key responsibilities include:
Develop Predictive Models for Drug Discovery
Independently Design and implement machine learning models to predict compound activity, selectivity, and developability.
Identify and Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.
Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.
Evaluate model performance and apply appropriate validation strategies.
Work with data engineers and ML engineers to integrate models into discovery pipelines.
Analyze Complex Scientific Data.
Perform exploratory data analysis on chemical, biological, and phenotypic datasets.
Integrate heterogeneous datasets including:
Chemical structure and screening data.
Structural biology and molecular simulation outputs.
Collaborate with Research Scientists.
Partner with medicinal chemists to support compound design and lead optimization.
Work with biologists to interpret experimental results and identify new target opportunities.
Translate scientific questions into computational modeling strategies.
Required Skills, Experience and Education:
PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.
7-9 years experience applying machine learning or advanced analytics to scientific datasets.
Python and scientific computing libraries (NumPy, Pandas, SciPy).
Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).
Model development, validation, and evaluation methods.
Data visualization and exploratory analysis.
Experience working with noisy and incomplete experimental datasets.
Preferred Skills:
Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).
Multi-omics data analysis.
Cloud computing environments.
MLOps or scalable model deployment.
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 [email protected].
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 [email protected] so we can share these impersonations with our IT team for tracking and awareness.
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