Computational Materials Science ML Expert
Listing checked October 29, 2026 · pay as published by AfterQuery
Key details
- Pay
- $175/hr
- Location
- Remote
- Job type
- contract
- Posted
- April 16, 2026
Overview
AfterQuery, an early-stage Y Combinator-backed company, needs a researcher comfortable moving between machine learning and computational materials science without losing footing in either. Assignments arrive one project at a time and run 2 to 3 weeks, with an expected 10 to 20 hours per week for the work you accept. Datasets are atomistic, electronic or structural: property prediction, model training on DFT or molecular dynamics output, and materials informatics pipelines that connect ML to high-throughput computation. Methods, assumptions and results all need clear written documentation, so expect part of the job to be explaining what your model does and why.
What You'll Do5
- Train and test ML models on simulation output drawn from DFT, molecular dynamics or Monte Carlo runs
- Apply supervised and unsupervised learning to predict materials properties, surface new candidates, and map structure to behavior
- Wire machine learning steps into high-throughput computational workflows for materials informatics
- Check model performance against held-out data and report where predictions break down
- Record methods, assumptions and technical choices so another researcher can reproduce the work
Requirements7
- At least one first-author paper published in a peer-reviewed journal
- Proven depth in both machine learning and computational materials science, shown through DFT, force field development, atomistic simulation or materials informatics
- Master's or PhD in Materials Science, Computational Chemistry, Physics, Computer Science or another quantitative field
- Record of solving technical problems without close supervision
- Preferred: prior TA or teaching work in computational physics, chemistry or materials science
- Preferred: hands-on use of materials ML frameworks such as CGCNN, MatGL, M3GNet, ALIGNN or similar graph neural network methods
- Preferred: experience with VASP, Quantum ESPRESSO, LAMMPS, ASE or comparable computational materials tools
Who Should Apply
A published researcher with first-author credit who pairs ML model building with hands-on DFT or atomistic simulation work. You hold a Master's or PhD in materials science, computational chemistry, physics or a related quantitative field and can carry a technical task from start to finish on your own.
Salary Insight
The listing posts $175/hr, with project rates falling in the $150 to $200 per hour band. That lands well above what academic postdoc or research assistant work usually pays per hour, which matches the bar set here: a PhD-level researcher with a first-author publication and working ML skills. Pay attaches to individual projects rather than a salaried schedule, so total earnings depend on how many assignments you take on.
Pay and demand for Machine Learning & AI roles
Market estimates from live NearSkill listings — separate from this job's published terms.
This role · as published
$175/hr
Taken from the listing itself.
Typical pay · estimated
$80/hour
Median of 575 similar roles that publish pay · 93 publish only a top rate; those count at the rate they gave
Most Machine Learning & AI roles pay $55–$100 per hour. This role's pay sits above that range.
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
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Most requested skills · share of roles
- python24%
- code review13%
- llm evaluation11%
- ai evaluation11%
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