ZipRecruiter

Log In

1

Machine Learning Computational Chemistry Jobs (NOW HIRING)

1910

Computational Theoretical Chemist II

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 * Partner to ...

previous page

Showing results 41-60

All JobsMachine Learning Computational Chemistry Jobs

Machine Learning Computational Chemistry information

See salary details

$24.5K

$114.5K

$211.5K

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

As of Aug 12, 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.

Is computational chemistry in demand?

Computational chemistry is in high demand within industries such as pharmaceuticals, materials science, and chemical research, where it supports drug discovery and molecular modeling. Professionals with skills in machine learning, programming, and chemistry are increasingly sought after to develop advanced simulation tools and analyze complex data sets.

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 August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $114,469 per year, or $55 per hour.

ML Engineer, Biological Analysis & Simulation

Mithrl

San Francisco, CA • On-site

Full-time

Re-posted 15 days ago

Job description

Job Summary:
Mithrl is building the world’s first commercially available AI Co-Scientist, transforming messy biological data into insights. The ML Engineer will develop core analytical layers for the AI, focusing on interpreting biological datasets and integrating simulation tools for drug discovery.
Responsibilities:
• Build AI driven analysis agents that perform biological reasoning across a wide range of datasets
• Develop the standard analysis suite for each dataset, including modules for differential expression, pathway analysis, feature importance, clustering, scoring, enrichment, and mechanism-of-action interpretation
• Build multi step workflows that combine ML models, statistical logic, and biological knowledge to produce high confidence insights
• Design and implement agentic reasoning strategies that allow Mithrl to run dozens analyses per dataset and synthesize the outputs into a coherent scientific narrative
• Integrate simulation and modeling tools for small molecule drug discovery, including ADMET prediction, docking scoring, generative chemistry tools, structure based modeling, and related computational frameworks
• Collaborate with the data engineering, bioinformatics, and curation teams to ensure analysis modules operate on clean and consistent data
• Validate results, benchmark pipelines, and ensure scientific accuracy and reproducibility of all analyses
• Contribute to the long term architecture for how the AI Co-Scientist performs reasoning, hypothesis testing, and simulation
Qualifications:
Required:
• Strong experience in machine learning, computational biology, or a related scientific ML field
• Experience developing analysis modules for biological or scientific datasets
• Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling
• Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models
• Proficiency in Python and scientific computing libraries
• Experience designing multi step reasoning or workflow based ML pipelines
• Ability to translate messy scientific questions into structured ML or analytical workflows
• Strong communication skills and comfort collaborating with cross functional scientific and engineering teams
Preferred:
• Experience with LLM powered scientific agents or multi agent architectures
• Familiarity with phenotype based discovery, multi modal integration, or systems biology
• Background in computational chemistry or structure based drug discovery
• Experience with biological ontologies, curated knowledge graphs, or pathway databases
• Prior experience in a tech bio company, biotech R&D group, or scientific platform team
Company:
Mithrl is a software development company that builds the custom workflows for NGS data on-demand. Founded in 2023, the company is headquartered in San Francisco, USA, with a team of 51-200 employees. The company is currently Early Stage.

Apply