Principal Machine Learning Scientist, Drug Discovery Analytics at Revolution Medicines

Ideal for a scientist with a PhD and 8+ years of experience applying machine learning or advanced analytics to scientific problems, particularly within drug dis

Work type: hybrid

Location: Redwood City, California, United States

Salary: $273,000 – $321,000/yr

Type: Full-time

Summary

Ideal for a scientist with a PhD and 8+ years of experience applying machine learning or advanced analytics to scientific problems, particularly within drug discovery, and with strong expertise in Python-based ML ecosystems and deep learning. **What makes it worth a look...** This full-time, hybrid role at Revolution Medicines in Redwood City, California offers a base salary of USD 273,000 - 321,000 per year, focusing on accelerating small-molecule drug discovery through advanced ML. **You might be a good fit if you...** * Have a PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline. * Possess strong expertise in Python ML ecosystems (PyTorch, TensorFlow, scikit-learn), data analysis (NumPy, Pandas), and deep learning. * Have demonstrated experience working with chemical or biological datasets in drug discovery or related fields. * Understand early-stage drug discovery workflows and can translate biological/chemical questions into computational frameworks.

Job Description

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company’s RAS(ON) inhibitors daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

The Opportunity:

We are seeking a Principal Machine Learning Scientist to lead the development of advanced machine learning approaches that accelerate small-molecule drug discovery. This role sits at the intersection of data science, chemistry, and biology, transforming complex scientific datasets into predictive models that guide target discovery, compound design, and translational hypotheses.

Working closely with experimental scientists, the Principal ML Scientist will develop cutting-edge modeling approaches that integrate chemical, biological, and phenotypic data. The successful candidate will play a key role in advancing a data-driven discovery strategy by designing predictive models, deploying innovative algorithms, and translating insights into actionable decisions that improve the speed and success of the discovery of medicines for patients with RAS-driven cancers.

Key responsibilities include:

Scientific Leadership:

• Define and lead machine learning strategies that accelerate early-stage drug discovery.

• Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making.

• Drive the adoption of innovative modeling approaches within multidisciplinary discovery teams.

• Develop predictive models for:

• Compound activity, selectivity, ADME/Tox, and developability properties.

• Target engagement, mechanism-of-action, and phenotypic datasets.

• Graph neural networks.

• Deep learning for molecular representation.

• Generative chemistry models.

• Active learning frameworks for experimental design.

• Partner with medicinal chemists to guide compound design and optimization.

• Work with biologists to interpret complex experimental datasets and generate mechanistic hypotheses.

• Collaborate with data scientists and engineers and ML engineers to deploy models into scalable discovery workflows.

• Integrate heterogeneous datasets including:

• Chemical structure and screening data.

• Imaging and phenotypic screening data.

• Structural biology and molecular simulation outputs.

• PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline.

• 8+ years experience applying machine learning or advanced analytics to scientific problems.

• Demonstrated experience working with chemical or biological datasets in drug discovery or related domains.

• Strong expertise in:

• Python-based ML ecosystems (PyTorch, TensorFlow, scikit-learn).

• Data analysis and scientific computing (NumPy, Pandas).

• Deep learning and representation learning techniques.

• Strong understanding of early-stage drug discovery workflows.

• Ability to translate biological or chemical questions into computational frameworks and predictive models.

• Proven ability to communicate complex computational insights to.

• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

• Experience with cheminformatics or bioinformatics toolkits is highly desirable.

• Familiarity with cloud computing and scalable ML workflows is a plus

• Ability to work at the interface of computational and experimental science.

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.

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