Materials ML Research Benchmark Specialist

IXO is engaging specialists to evaluate machine-learning models that make meaningful use of computational materials data. Your contribution is technical work: make the reasoning inspectable, identify substantive errors and provide evidence that supports a reliable assessment.

Work you will do

• Build or evaluate models for property prediction, materials discovery or structure–property relationships.
• Work with DFT, molecular-dynamics, Monte Carlo or related simulation outputs, checking how the data supports the learning objective.
• Develop reproducible materials-informatics pipelines and document the modeling assumptions, evaluation and scientific interpretation.

Required background and routes

• A relevant master's or doctorate and at least one first-author paper in a peer-reviewed journal.
• Demonstrated ML expertise combined with computational materials practice, such as force fields, DFT, atomistic simulation or materials informatics; independent technical problem solving.

Preferred background

• Materials/physics/chemistry teaching, materials GNN frameworks such as ALIGNN, M3GNet, MatGL or CGCNN, and computational tools such as ASE, LAMMPS, Quantum ESPRESSO or VASP.

Deliverables

Submit the completed technical artifact or assessment with its supporting evidence, explicit assumptions, reproducible checks where applicable, and concise reasons for each material judgment. Address review findings within the agreed scope.

Location and schedule

Remote assignments are scheduled by agreement, with no guaranteed weekly volume. Availability planning can include 10–20 hours per week depending on the track. Indicative assignment lengths include 2–3 weeks. IXO confirms the applicable timing before work.

Pay and working terms

$160 $210/hr USD. The agreed hourly rate, scope, schedule and acceptance criteria are confirmed before work starts. Applying does not guarantee an assignment. Use public, licensed or otherwise authorized material only; do not submit confidential employer information, personal data or restricted research.