Design, evaluate, and critique chemistry and computational chemistry problems, workflows, and reasoning chains to support AI benchmarking initiatives.
Provide expert-level insights on reaction mechanisms, synthesis planning, spectroscopy analysis, molecular simulations, and electronic structure methods.
Assess and optimize experimental protocols, simulation parameters, and software configurations for accuracy and reproducibility.
Analyze chemical, spectroscopic, and simulation data and provide 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.
Areas of Expertise (we welcome applicants from any of the following):
Organic synthesis & reaction mechanisms
Inorganic & organometallic chemistry
Physical & theoretical chemistry
Analytical chemistry & spectroscopy
Computational chemistry & molecular dynamics
Cheminformatics & drug discovery
Materials science & polymer chemistry
Required Skills and Qualifications:
PhD
in chemistry, computational chemistry, chemical biology, materials science, or a closely related field.
Hands-on research lab experience
, including wet lab experience for experimental subfields, or extensive simulation-heavy research for computational subfields.
Deep expertise in at least one of the areas listed above.
Professional written and spoken English (C1 or higher).
Proficiency in Python and/or industry-standard tools in your subfield (e.g., Gaussian, ORCA, Psi4, GROMACS, LAMMPS, RDKit).
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.
Academic experience
(postdoc, research scientist, faculty, or equivalent), strongly preferred.
Peer-reviewed publications, patents, or contributions to open-source scientific software.
Experience with AI/ML-assisted chemistry workflows or benchmarking.
Background in drug discovery, reaction prediction, or retrosynthesis.
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