Computational Materials Science Expert

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

Employment type: Contract Time commitment: 10 hours/week Location: Remote