Computational Materials Science Expert

This is a remote, project-based role for machine learning professionals with deep expertise in computational materials science. You will complete tasks at the intersection of ML and materials research — including model development, simulation data analysis, and research tasks applied to real atomistic, electronic, or structural materials datasets. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge materials research problems, and a strong addition to your research portfolio.

Responsibilities - Apply machine learning techniques to materials science problems, including property prediction, materials discovery, and structure-property relationship modeling - Build, train, and evaluate ML models trained on simulation data such as DFT, molecular dynamics, or Monte Carlo outputs - Develop predictive models using supervised and unsupervised learning approaches relevant to materials systems - Contribute to materials informatics workflows integrating ML with high-throughput computational pipelines - Document methodologies, model assumptions, and technical approaches clearly and reproducibly

Required qualifications - Published researcher with at least one first-author publication in a peer-reviewed journal - Demonstrated expertise in both machine learning and computational materials science (e.g., DFT, force field development, atomistic simulation, or materials informatics) - Strong problem-solving skills and ability to work independently on technical tasks - Master's or PhD in Materials Science, Computational Chemistry, Physics, Computer Science, or a related quantitative field

Preferred qualifications - Background in TA'ing or teaching computational physics, chemistry, or materials science courses - Familiarity with materials-focused ML frameworks (e.g., CGCNN, MatGL, M3GNet, ALIGNN, or similar graph neural network approaches) - Experience with computational materials tools and frameworks (e.g., VASP, Quantum ESPRESSO, LAMMPS, ASE, or similar)

Why apply - Flexible Time Commitment – Work on your schedule while tackling meaningful scientific challenges - Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart - Exceptional Pay – Project-based pay ranges from $150–$200/hour - Portfolio Building – Gain experience applying ML to frontier computational materials problems - Professional Growth – Sharpen your skills on complex, real-world materials datasets and models