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Machine Learning Computational Chemistry Jobs in Berkeley, CA

Lam Research

Computational Senior Engineer

Fremont, CA · On-site

$119K - $261K/yr

... chemistry learning to support process engineering research & development, and process/chamber ... D in Computational Chemistry, Chemistry, Chemical Engineering or Materials Science (or equivalent ...

Gilead

Sr Analyst, Data Scientist

Foster City, CA · On-site

Bachelor's degree with at least 4 years of relevant experience i n Data Science, Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Statistics, Chemical Engineering ...

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All JobsMachine Learning Computational Chemistry JobsMachine Learning Computational Chemistry Jobs in Berkeley, CA

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 4, 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.

Scientist, Machine Learning

Atomic AI

South San Francisco, CA

$170K - $220K/yr

Full-time

Re-posted 8 days ago

Job description

At Atomic AI, we build artificial intelligence to pioneer new frontiers in drug discovery. Our unique R&D platform, an early version of which was featured on the cover of Science, provides new strategies to treat previously undruggable diseases by targeting RNA. We continue to advance this platform by developing new machine learning methods and unique foundation models fueled by our large-scale, in-house experimental data collection. We are an interdisciplinary team of scientists and engineers and believe our people are our greatest strength and the key to our success.

The opportunity

As a full-time Scientist on the Machine Learning team, you will work closely with engineers and experimental scientists to advance our technology platform for RNA structure prediction, target identification, and early drug discovery. You will co-lead the development and evaluation of the machine learning pipeline. You will contribute new ideas and realize their potential as part of a continuously advancing state-of-the-art platform. You will proactively shape the directions of the machine learning efforts and those of the whole company. 

Primary responsibilities

  • Design and develop novel machine learning models for RNA structure prediction and drug targeting.
  • Evaluate and advance the state of the art of our structure prediction platform.
  • Collaborate with our wetlab team on the targeted acquisition of experimental data to improve our machine learning models.
  • Develop high-quality code in a team setting.
  • Analyze, interpret, and organize results and present progress to colleagues in regular research meetings.
  • Work within a collaborative, high-caliber, interdisciplinary team and proactively shape the scientific and strategic vision of the company.

About you

  • Ph.D., M.Sc., or M.Eng. in Computer Science, Physics, Applied Mathematics, Materials Science, Computational Biology, or related field.
  • 4+ years of experience developing machine learning methods for scientific applications.
  • Foundational knowledge of machine learning and underlying mathematical concepts.
  • Proficiency in Python and deep learning frameworks (e.g., JAX, PyTorch).
  • Excellent presentation and writing skills, able to clearly communicate technical information to colleagues.

Pluses

  • Publications at major machine learning conferences or in major scientific journal
  • Research experience related to structural biology, molecular design, and drug discovery.
  • Foundational knowledge of physics, chemistry, and molecular biology.
  • Demonstrated ability to develop performant code.

Salary Range (all levels): $170,000/year to $220,000/year + equity + benefits. This range reflects variations in seniority, expertise, and skills.

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About Atomic AI

Sourced by ZipRecruiter

Industry

Biotechnology research and development

Company size

11 - 50 Employees

Headquarters location

South San Francisco, CA, US

Year founded

2021

Social media

Twitter

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