In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Key Responsibilities
Design, evaluate, and critique advanced computational chemistry workflows to support AI benchmarking initiatives.
Provide expert-level insights on molecular simulations, electronic structure methods, and cheminformatics pipelines.
Assess and optimize simulation parameters and scientific software configurations for accuracy and reproducibility.
Analyze data from molecular dynamics, quantum chemistry, and drug discovery projects, providing actionable recommendations.
Leverage domain knowledge to guide the development and validation of chemistry-related AI models and tools.
Communicate complex scientific concepts clearly, both in writing and verbally, to technical and non-technical collaborators.
Document findings, methodologies, and best practices to ensure knowledge transfer across the customer’s team.
Required Skills and Qualifications
PhD in chemistry, computational chemistry, or closely related field, or equivalent industry/research experience.
Deep expertise with computational chemistry methods and simulation-heavy environments is desirable.
Proficiency with industry-standard tools such as Gaussian, ORCA, Psi4, NWChem, GROMACS, LAMMPS, AMBER, RDKit, or OpenBabel would be ideal.
Strong scientific programming and analytical workflow experience, especially using Python.
Excellent quantitative, analytical, and scientific reasoning skills.
Outstanding written and verbal communication abilities, with the ability to clearly document and explain technical concepts.
Track record of rigorous scientific problem-solving in complex chemistry domains.
Preferred Qualifications
Experience with AI/ML-assisted chemistry workflows or benchmarking.
Peer-reviewed publications, patents, or contributions to open-source scientific software.
Background in drug discovery, spectroscopy analysis, or reaction prediction/retrosynthesis.