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How much do cheminformatics jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for cheminformatics in California is $55.63, according to ZipRecruiter salary data. Most workers in this role earn between $47.08 and $67.32 per hour, depending on experience, location, and employer.

How to get into cheminformatics?

To enter cheminformatics, a background in chemistry, computer science, or bioinformatics is essential, along with skills in programming languages like Python or R and familiarity with chemical data formats. Gaining experience through internships, online courses, or advanced degrees such as a master's or Ph.D. can improve job prospects. Knowledge of cheminformatics tools and databases, such as RDKit or ChemAxon, is also beneficial.

What skills and qualifications are needed for cheminformatics?

To thrive in Cheminformatics, you need a solid background in chemistry, computer science, and data analysis, often supported by a relevant degree in chemistry, bioinformatics, or a related field. Proficiency with cheminformatics software (e.g., RDKit, Open Babel), programming languages like Python or Java, and familiarity with databases such as SQL is highly valued. Strong problem-solving skills, teamwork, and clear scientific communication are essential soft skills for success in this interdisciplinary field. These skills ensure the effective development and application of computational tools to solve complex chemical and pharmaceutical research challenges.

What is a cheminformatics?

A Cheminformatics job involves using computational techniques, data analysis, and software tools to manage, analyze, and interpret chemical and molecular data. Professionals in this field apply machine learning, molecular modeling, and database management to support drug discovery, materials science, and other chemistry-related research. They often work in pharmaceutical, biotech, or academic settings, collaborating with chemists and data scientists to accelerate scientific discovery.

What does a cheminformatics do?

In a Cheminformatics position, you'll typically work on projects involving the analysis and management of chemical data, such as building and evaluating molecular models, developing algorithms for virtual screening, or optimizing compound libraries for drug discovery. Daily tasks often include programming, data visualization, collaborating with medicinal chemists or biologists, and contributing to research publications or presentations. You may also be responsible for maintaining database integrity and ensuring the quality of datasets used in computational experiments. The work is both collaborative and analytical, offering exposure to cutting-edge technology in chemical and pharmaceutical research environments.

What are the most commonly searched types of Cheminformatics jobs in California? The most popular types of Cheminformatics jobs in California are:
What are popular job titles related to Cheminformatics jobs in California? For Cheminformatics jobs in California, the most frequently searched job titles are:
What job categories do people searching Cheminformatics jobs in California look for? The top searched job categories for Cheminformatics jobs in California are:
What cities in California are hiring for Cheminformatics jobs? Cities in California with the most Cheminformatics job openings:
Cheminformatics jobs near you
Infographic showing various Cheminformatics job openings in California as of August 2026, with employment types broken down into 13% Full Time, and 87% Part Time. Highlights an 7% Physical, 1% Hybrid, and 92% Remote job distribution, with an average salary of $115,710 per year, or $55.6 per hour.

ML & Molecular Simulation Scientist

Genesis Molecular AI

San Mateo, CA • On-site

Full-time

Re-posted 28 days ago

Job description

Job Summary:
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. They are seeking a ML & Molecular Simulation Scientist to develop and apply methods at the intersection of 3D molecular simulation and machine learning, contributing to impactful drug discovery programs.
Responsibilities:
• Build and apply ML models informed by 3D structural data, including geometric deep learning, equivariant neural networks, and diffusion-based generative models for molecular design and property prediction
• Integrate physics-based and ML + data-driven approaches, combining force field methods, quantum chemistry, and structure-based design with modern ML to improve accuracy and throughput
• Develop and apply simulation methods spanning molecular dynamics, enhanced sampling (metadynamics, replica exchange, umbrella sampling), and free energy calculations (FEP/TI) to support active drug discovery programs
• Contribute to the GEMS platform, improving our generative AI and scoring capabilities, focusing on 3D methods; strengthen ML and physics-based scoring functions (and their intersection), build next-gen force fields
• Work directly with CADD and discovery scientists to apply computational methods across the drug discovery pipeline, from target structure analysis through lead optimization
• Stay current with the field, implementing and adapting methods from the latest literature in geometric ML, biomolecular simulation, and computational drug design
• Communicate scientific results clearly to multidisciplinary teams, including experimental chemists and biologists
Qualifications:
Required:
• Practical experience with 3D machine learning – geometric deep learning, graph neural networks, equivariant architectures (e.g., SE(3)/E(3) networks), or diffusion models applied to molecular data
• PhD (preferred) in computer science, machine learning, chemical engineering, biophysics, physics, or a closely related field; postdoctoral or industry experience is a plus
• Deep, hands-on expertise in molecular simulation, including MD, enhanced sampling, and/or free energy methods using tools such as GROMACS, AMBER, OpenMM, or NAMD
• Familiarity with structure-based drug design workflows: docking, binding site analysis, protein-ligand interaction modeling using tools such as MOE, or PyMOL
• Proficiency in Python and scientific computing libraries (PyTorch, JAX, NumPy, MDAnalysis, RDKit); comfort with HPC environments and scripting for large-scale simulation workflows
• A track record of applying computational methods to real scientific problems, demonstrated through publications, open-source contributions, or industry impact
• Collaborative, curious, and able to move between rigorous method development and fast-paced discovery work
Preferred:
• Familiarity with cheminformatics and ADMET property prediction
• Contributions to open-source simulation or ML tooling
Company:
Genesis Therapeutics unifies AI and biotech to accelerate the discovery of new medicines. Founded in 2019, the company is headquartered in South San Francisco, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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