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Machine Learning Computational Chemistry Jobs (NOW HIRING)

Genesis Therapeutics

Product Management Lead

Manhattan, NY · On-site

$120 - $190/hr

Our work spans machine learning, computational chemistry, biology, and wet lab experimentation , all operating within a high-trust, deeply technical environment. You'll join a team with extremely ...

Genesis Therapeutics

Product Management Lead

San Mateo, CA · On-site

$120 - $190/hr

Our work spans machine learning, computational chemistry, biology, and wet lab experimentation , all operating within a high-trust, deeply technical environment. You'll join a team with extremely ...

Genesis Therapeutics

Product Management Lead

San Diego, CA · On-site

$120 - $190/hr

Our work spans machine learning, computational chemistry, biology, and wet lab experimentation , all operating within a high-trust, deeply technical environment. You'll join a team with extremely ...

1910

Computational Theoretical Chemist III

Boston, MA · On-site

Teach key computational chemistry principles to your cross-disciplinary colleagues from Medicinal Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology * Manage day-to ...

1910

Computational Theoretical Chemist III

Boston, MA · On-site

Teach key computational chemistry principles to your cross-disciplinary colleagues from Medicinal Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology * Manage day-to ...

1910

Computational Theoretical Chemist I

Boston, MA

Teach key computational chemistry principles to your cross-disciplinary colleagues from Medicinal Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology * Partner to ...

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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 Sep 2, 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 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.

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Machine Learning Computational Chemistry jobs near you
Infographic showing various Machine Learning Computational Chemistry job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $114,469 per year, or $55 per hour.

Computational Theoretical Chemist II

1910

Boston, MA

Full-time

Re-posted 8 days ago

Job description

Computation is revolutionizing drug discovery. Advances in big chemical data, massive computing power, artificial intelligence, and molecular dynamics simulation are changing the way we develop new drugs. At 1910 , we put computation at the heart of drug discovery, blending expertise in computational chemistry, structural biology, pharmacology, data science, and software engineering to develop drugs for previously undruggable targets. 

Role Description 

  • Own computational chemistry programs across therapeutic modalities, disease targets, and indications 
  • Ensure effective collaboration with the Biology and Medicinal Chemistry teams by providing key computational chemistry insights to aid in the Hit-to-Lead and Lead Optimization phases of drug discovery operations  
  • Ensure effective collaboration with the ML Engineering and AI Research team by providing key computational chemistry insights to aid in the development of AI/ML models for drug discovery as well as the incorporation of those models into drug discovery operations 
  • Teach key computational chemistry principles to your cross-disciplinary colleagues from Medicinal Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology 
  • Partner to improve 1910's existing process for progressing from computational hit to experimental hit to lead to drug candidate 
  • Co-author provisional patents and peer-reviewed research papers 
  • Progress a virtual hit to a biochemical/cellular hit 
  • Validate a cellular hit in a clinically relevant animal model of disease 
  • Update provisional patents with the animal model data 
  • Nominate a lead candidate for progression into IND-enabling studies 
  • Attend and present research at conferences and events related to computational modeling in drug discovery 

Qualifications  

  • Ph.D. in computational chemistry or related discipline 
  • 2 years of relevant industry experience within drug discovery or biotechnology 
  • Played a key role in advancing a drug discovery program from early research phases to clinical development 
  • In-depth knowledge and hands-on experience with quantum chemical (QC) methods, including semi-empirical and density functional theory (DFT) approaches, molecular dynamics (MD) simulations, including both standard MD and enhanced sampling techniques such as metadynamics, umbrella sampling, and replica exchange MD, free energy simulations such as FEP and TI, and QM/MM methodologies for small and large molecular systems 
  • Strong understanding of key concepts, including potential energy surfaces (PES), intermolecular and intramolecular forces/interactions, force fields, molecular properties, thermodynamic properties, solvation models (implicit/explicit), and conformational sampling 
  • Proficiency in analyzing molecular properties such as solvation free energy, dipole moments, vibrational frequencies, electrostatic potential, charge distribution, and more. 
  • Deep knowledge of implicit and explicit solvent models, with extensive experience modeling solvent effects on molecular systems and chemical reactions in various environments 
  • Extensive experience in using and troubleshooting software tools for QC calculations (e.g., ORCA, xTB, CREST, etc.), MD simulations (e.g., GROMACS, OpenMM, etc.), Drug Design Development Packages (e.g., EG, Schrodinger, MOE, CRESSET) 
  • Experience working with HPC Clusters and cloud-based services like (e.g., Microsoft AZURE, AWS) 
  • Ability to optimize computational simulation protocols for efficient resource usage 
  • Proven experience working with small organic molecules and large biomolecular systems (e.g., peptides, proteins, etc.) for property prediction, conformational analysis, and structure-activity relationships (SAR) 
  • Hands-on experience with Python and Bash scripting for automating workflows and data analysis 
  • Familiarity with cheminformatics toolkits such as RDKit for molecular property prediction and data management 
  • Basic knowledge of machine learning (ML) techniques applied to molecular property prediction, virtual screening, and related tasks 
  • Strong desire to collaborate with AI scientists, data scientists, medicinal chemists, and biologists to interpret computational results and guide experimental design 
  • Clear and effective communication of complex scientific ideas through reports, presentations, and publications 

Nice to Haves 

  • Publications in computational chemistry related to drug discovery 

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