Computational Chemist for AI Model Training

Apply advanced computational chemistry knowledge to design, evaluate, and optimize molecular simulation and electronic structure workflows that train and benchmark next-generation AI systems. You will analyze simulation and quantum chemistry results, improve scientific software configurations for reproducibility and accuracy, and document methods and recommendations that inform chemistry-related AI model development.

• Design, evaluate, and critique computational chemistry workflows used for AI benchmarking and training.
• Provide expert input on molecular simulations, electronic structure methods, and cheminformatics pipelines.
• Assess and optimize simulation parameters and scientific software configurations to improve accuracy and reproducibility.
• Analyze data from molecular dynamics, quantum chemistry, and drug discovery projects, producing actionable recommendations.
• Guide development and validation of chemistry-related AI models and tools using domain expertise.
• Communicate complex scientific concepts clearly in writing and verbally to technical and non-technical collaborators.
• Document findings, methodologies, and best practices to ensure knowledge transfer across the customer team.

Required

• PhD in chemistry, computational chemistry, or a closely related field, or equivalent industry or research experience.
• Deep expertise with computational chemistry methods and simulation-heavy environments.
• Proficiency with industry tools and packages, such as Gaussian, ORCA, Psi4, NWChem, GROMACS, LAMMPS, AMBER, RDKit, and OpenBabel.
• Strong scientific programming experience, especially in Python, and familiarity with analytical workflows.
• Excellent quantitative, analytical, and scientific reasoning skills.
• Outstanding written and verbal communication, and proven ability to document and explain technical concepts.
• Demonstrated scientific problem-solving in complex chemistry domains.

Preferred

• Experience with AI or ML assisted chemistry workflows and benchmarking.
• Peer-reviewed publications, patents, or contributions to open-source scientific software.
• Background in drug discovery, spectroscopy analysis, reaction prediction, or retrosynthesis.

• Position type: Contractor, independent contractor engagement.
• Location: Remote.
• Compensation model: Hourly pay, paid on an hourly basis.

• Pay range: 40 to 60 hourly.

• Must hold a PhD in a relevant field or have equivalent industry or research experience, as stated above.

If you are interested, follow the application controls on the posting to indicate your interest and provide the requested materials. The posting will capture your experience, availability, and any other details required to evaluate fit for contractor engagements.