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

Where you can work

This role is 100% remote: you can work from any country except Afghanistan, Argentina, Bangladesh, Burundi, Bolivia, Brazil, Belarus, DR Congo, Central African Republic, Congo (Republic), Chile, China, Colombia, Cuba, Dominican Republic, Egypt, Eritrea, Ghana, Gibraltar, Haiti, Iraq, Iran, Japan, Cambodia, Comoros, North Korea, South Korea, Libya, Monaco, Myanmar, Macau, Nigeria, Nicaragua, Palestine, Paraguay, Russia, Saudi Arabia, Sudan, Somalia, South Sudan, Syria, Chad, Turkmenistan, Uruguay, Venezuela, Yemen and Zimbabwe.