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Machine Learning Computational Chemistry information

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

As of Jul 21, 2026, the average yearly pay for machine learning computational chemistry in the United States is $114,469.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,000.00 and $154,500.00 per year, depending on experience, location, and employer.

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 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.
More about Machine Learning Computational Chemistry jobs
What cities are hiring for Machine Learning Computational Chemistry jobs? Cities with the most Machine Learning Computational Chemistry job openings:
What states have the most Machine Learning Computational Chemistry jobs? States with the most job openings for Machine Learning Computational Chemistry jobs include:
What job categories do people searching Machine Learning Computational Chemistry jobs look for? The top searched job categories for Machine Learning Computational Chemistry jobs are:
Machine Learning Computational Chemistry jobs near you
Infographic showing various Machine Learning Computational Chemistry job openings in the United States as of July 2026, with employment types broken down into 1% Internship, 1% As Needed, 79% Full Time, 16% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution, with an average salary of $114,469 per year, or $55 per hour.
Senior Scientist - Principal Scientist, Computational Chemistry

Senior Scientist - Principal Scientist, Computational Chemistry

Superluminal Medicines, Inc.

Boston, MA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 2 days ago

Job description

About Superluminal Medicines:
Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company's platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.
About the Role:
We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions and develop necessary workflows to drive small molecule drug discovery. You will serve as a core strategic partner to medicinal chemists and biologists, focusing on compound design and tool development to impact discovery pipeline and address unmet computational needs.
Key Responsibilities:
  • Integrate physics-based simulations with ML predictions to achieve the quantitative accuracy required to prioritize compounds for synthesis
  • Collaborate with a team of interdisciplinary scientists to develop actionable hypotheses and design computational experiments
  • Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection
  • Develop, validate and deploy computational workflows to optimize the "Design-Make-Test-Analyze" cycles and address gaps

Required Qualifications:
  • Ph.D. in Computational Chemistry, Biophysics, or a related field
  • 1-3+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery
  • Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact
  • Exceptional ability to communicate the "why" behind a design to a diverse scientific audience
  • Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc
  • A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones

Preferred Qualifications:
  • Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques
  • Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization
  • A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones

Skills & Competencies:
  • Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, OpenEye, MOE, etc)
  • Ability to work directly in a Linux-based environment
  • Familiarity with cloud computing infrastructure (AWS, GCS) is a plus
  • Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc)

Benefits:
Superluminal offers a comprehensive benefits package that fully covers employees' annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.
Equal Opportunity Statement:
Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.

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