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Learning Scientist Jobs in California (NOW HIRING)

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Learning Scientist information

What is a learning scientist?

A Learning Scientist studies how people learn and applies research to improve education, training, and instructional design. They combine insights from cognitive science, psychology, education, and technology to develop effective learning environments. Their work may involve designing digital learning tools, analyzing learner behavior, and conducting research to enhance teaching methods. Learning Scientists often collaborate with educators, designers, and policymakers to optimize learning experiences in schools, workplaces, and online platforms.

What types of projects does a learning scientist typically work on within an organization?

Learning Scientists often design and evaluate educational programs, develop new instructional methods, or study how people learn in various environments. Their projects may include collaborating with educators to create digital learning tools, running experiments to measure student engagement or comprehension, and analyzing large data sets to assess the effectiveness of teaching strategies. Many Learning Scientists work closely with curriculum developers, technologists, and policymakers to implement and scale evidence-based improvements. This role provides continuous opportunities to tackle complex, real-world learning challenges while making a measurable impact on educational outcomes.

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

To thrive as a Learning Scientist, you need a solid background in educational psychology, research methods, data analysis, and often a doctoral degree in a related field. Familiarity with learning management systems (LMS), statistical software (such as SPSS or R), and experience conducting experiments or large-scale educational studies is valuable. Strong communication, critical thinking, and collaborative skills help Learning Scientists effectively share insights and work across multidisciplinary teams. These skills are essential for designing evidence-based interventions, analyzing learning outcomes, and driving educational innovation.

What are popular job titles related to Learning Scientist jobs in California?

For Learning Scientist jobs in California, the most frequently searched job titles are:

What job categories do people searching Learning Scientist jobs in California look for?

The top searched job categories for Learning Scientist jobs in California are:

What cities in California are hiring for Learning Scientist jobs?

Cities in California with the most Learning Scientist job openings:

Learning Scientist jobs near you
Infographic showing various Learning Scientist job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Scientist II, Drug Discovery Analytics

Redwood City, CA

Revolution Medicines
See more jobs at Revolution Medicinesexternal

Full-time

Re-posted 19 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 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.

Apply