Computational Materials Scientist - AI Trainer

Training AI models for computational materials research is the focus of this remote AfterQuery contract. Bring your combined machine learning and materials science expertise to real atomistic, electronic, and structural datasets.

Research assignmentsBuild, train, and evaluate models using simulation data from DFT, molecular dynamics, or Monte Carlo methods. Apply supervised and unsupervised learning to property prediction, materials discovery, and structure–property relationships. Connect materials informatics workflows with high-throughput computational pipelines. Document methods, assumptions, and technical choices so results can be reproduced. Required research background Master’s or PhD in Materials Science, Computational Chemistry, Physics, Computer Science, or a related quantitative discipline. At least one first-author publication in a peer-reviewed journal. Demonstrated expertise in both machine learning and computational materials science, such as DFT, force fields, atomistic simulation, or materials informatics. Strong independent problem-solving skills. Helpful additional experience Teaching computational science; materials ML frameworks such as CGCNN, MatGL, M3GNet, or ALIGNN; and simulation tools such as VASP, Quantum ESPRESSO, LAMMPS, or ASE. Contract details $160–$180 per hour. Fully remote and asynchronous, approximately 10 hours per week, starting ASAP. Ongoing project-based engagement; individual assignments may run over 2–3 weeks, with hours agreed for the projects you accept. AfterQuery develops expert datasets and experiments that advance AI research. This work offers the opportunity to apply your research judgment to challenging materials problems while building your technical portfolio. Job Type: Contract Pay: $160.00 - $180.00 per hour Expected hours: 10.0 per week Work Location: Remote