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Machine Learning Computational Chemistry Jobs in Berkeley, CA

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Machine Learning Computational Chemistry information

See Berkeley, CA salary details

$27.4K

$127.9K

$236.2K

How much do machine learning computational chemistry jobs pay per year?

As of Sep 3, 2026, the average yearly pay for machine learning computational chemistry in Berkeley, CA is $127,862.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,009.00 and $172,576.00 per year, depending on experience, location, and employer.

What is machine learning computational chemistry?

Machine learning computational chemistry is a field that combines machine learning techniques with computational chemistry to accelerate the discovery and design of molecules and materials. By training algorithms on large datasets of chemical information, researchers can predict molecular properties, simulate chemical reactions, and optimize compounds more efficiently than traditional methods. This approach helps reduce the time and cost required for research in drug discovery, materials science, and related fields.

What are some common challenges faced by professionals working in machine learning computational chemistry roles?

One common challenge in Machine Learning Computational Chemistry roles is integrating large and often complex chemical datasets with appropriate machine learning models, which requires a solid understanding of both domains. Professionals may also encounter difficulties in ensuring that their models are both interpretable and generalizable to new data, as overfitting is a frequent issue. Additionally, collaboration with chemists and data scientists is essential, so clear communication across disciplines is key to success. Staying up to date with the latest developments in both computational chemistry and machine learning is crucial for ongoing professional growth.

What are the key skills and qualifications needed to thrive as a machine learning computational chemist, and why are they important?

To thrive as a Machine Learning Computational Chemist, you need a solid background in chemistry, mathematics, and computer science, typically supported by an advanced degree in computational chemistry, cheminformatics, or a related field. Proficiency with programming languages (such as Python), machine learning frameworks (like TensorFlow or PyTorch), and molecular modeling software is essential. Strong analytical thinking, problem-solving skills, and effective collaboration are key soft skills that help drive innovation and teamwork. These skills and qualifications are critical for developing accurate models, advancing research, and translating computational insights into real-world chemical solutions.

What is the difference between Machine Learning Computational Chemistry vs Computational Chemist?

AspectMachine Learning Computational ChemistryComputational Chemist
Required CredentialsAdvanced degrees in chemistry, computer science, or related fields; knowledge of machine learning and programmingDegree in chemistry, chemical engineering, or related fields; strong background in chemical theory and modeling
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm development and data analysisLaboratories, research institutions, industry; focus on chemical modeling and simulation
Employer & Industry UsageTech firms, pharmaceutical companies, research institutions applying AI/ML techniquesPharmaceutical, chemical, and materials industries conducting chemical research and development

Machine Learning Computational Chemists specialize in applying machine learning algorithms to chemical data, enhancing predictive models and simulations. Computational Chemists focus on traditional chemical modeling and simulations using computational methods. Both roles require strong chemistry backgrounds, but Machine Learning Computational Chemists emphasize data science and AI skills, while Computational Chemists focus on chemical theory and modeling techniques.

What are popular job titles related to Machine Learning Computational Chemistry jobs in Berkeley, CA?

For Machine Learning Computational Chemistry jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Computational Chemistry jobs in Berkeley, CA look for?

The top searched job categories for Machine Learning Computational Chemistry jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Machine Learning Computational Chemistry jobs?

Cities near Berkeley, CA with the most Machine Learning Computational Chemistry job openings:

Machine Learning Computational Chemistry jobs near you
Infographic showing various Machine Learning Computational Chemistry job openings in Berkeley, CA as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $127,862 per year, or $61.5 per hour.

Senior Machine Learning Scientist II, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA

Full-time

Re-posted 13 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