Machine Learning Scientist

• 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.

• PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.
• 6-10 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., Advertising, Analysis Skills, Analytical Chemistry, Artificial Intelligence (AI), Biology, Chemistry, Computational Chemistry, Computer Science, Data Analysis, Data Science, Data Sets, Deep Learning, Drug Development, Drug Discovery, Ecosystems, Machine Learning, Medical Products, Model Validation, Neural Networks, Oncology, Performance Modeling, Predictive Modeling, Purchasing/Procurement, Research & Development (R&D), Sales Prospecting, Scientific Research, Short Messaging Service (SMS), Statistics, Team Player, Training Data Sets, Translational Research

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.