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Lead Machine Learning Scientist Jobs in California

BigHat

Principal, Machine Learning Scientist

San Mateo, CA · On-site

$254K - $290K/yr

Principal, Machine Learning Scientist Department: DS/ML (Data Science/Machine Learning) Employment Type: Full Time Location: San Mateo, CA Reporting To: Hunter Elliot Description The role: We are ...

Profluent

Machine Learning Scientist, BioML

Emeryville, CA · On-site +1

$212K - $339K/yr

We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into models at the intersection of complex protein biology and AI. This position offers an opportunity to ...

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Lead Machine Learning Scientist information

What does a lead machine learning scientist do?

A Lead Machine Learning Scientist oversees the design, development, and deployment of machine learning models within an organization. They guide teams in identifying suitable algorithms, optimizing model performance, and ensuring solutions align with business goals. This role often involves collaborating with data engineers, analysts, and stakeholders to translate complex data problems into actionable insights. Additionally, Lead Machine Learning Scientists mentor junior staff and stay updated with the latest advancements in artificial intelligence and machine learning technologies.

What are the key skills and qualifications needed to thrive as a lead machine learning scientist?

To thrive as a Lead Machine Learning Scientist, you need deep expertise in machine learning algorithms, statistical analysis, data modeling, and typically a Ph.D. or Master’s degree in computer science, mathematics, or a related field. Proficiency with programming languages like Python or R, frameworks such as TensorFlow or PyTorch, and experience with cloud computing platforms are commonly required. Strong leadership, communication, and problem-solving abilities help in guiding teams and translating complex technical insights to stakeholders. These skills are vital for driving impactful AI solutions, fostering innovation, and ensuring successful project delivery.

How does a lead machine learning scientist typically collaborate with data engineers and product teams?

As a Lead Machine Learning Scientist, you will frequently work closely with data engineers to ensure data pipelines are robust, scalable, and optimized for model training and deployment. Collaboration with product teams is also essential to align ML solutions with business objectives, define project requirements, and interpret model outputs in a way that drives product improvements. Effective communication and cross-functional teamwork are crucial in this role, as you'll often need to translate complex technical concepts for non-technical stakeholders and guide interdisciplinary teams toward successful project outcomes.

What cities in California are hiring for Lead Machine Learning Scientist jobs?

Cities in California with the most Lead Machine Learning Scientist job openings:

Infographic showing various Lead Machine Learning Scientist job openings in California as of August 2026, with employment types broken down into 84% Full Time, 12% Part Time, and 4% Contract. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution.

Senior Machine Learning Scientist I, Drug Discovery Analytics

Redwood City, CA • Hybrid

Revolution Medicines
See more jobs at Revolution Medicinesexternal

$112K - $153K/yr

Full-time

Re-posted 17 days ago

Job description

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.

  • 6-10 years of 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. 

    #LI-Hybrid  #LI-LN1

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