Build and maintain strong baselines comparing composition-only and structure-aware representations across a set of key property prediction tasks.
Prototype and evaluate multi-view representation methods that integrate signals from multiple characterizations and properties.
Develop a reusable evaluation and reporting pipeline to assess generalization, identify failure modes, and quantify uncertainty where appropriate.
Curate datasets and implement preprocessing workflows that better capture complex materials systems and common sources of noise or ambiguity.
Contribute high-quality, well-documented code to an internal codebase and help translate results into internal reports, publications, and/or patent disclosures (as appropriate).

Requirements

MS or PhD (or equivalent industry experience) in computer science, applied math, materials science, chemistry, physics, or a related field.
Strong foundations in machine learning with demonstrated experience training models on real datasets.
Familiarity with modern scientific ML approaches, including representation learning, uncertainty estimation, and/or physics-informed or hybrid physics–ML modeling.
Proficiency in Python and modern ML tooling (e.g., PyTorch/JAX, experiment tracking, reproducibility best practices).
Ability to work collaboratively across disciplines and to translate research ideas into working code.

Benefits

medical insurance
dental insurance
vision insurance
paid time off
holiday pay
sick time

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

machine learningrepresentation learninguncertainty estimationphysics-informed modelinghybrid physics-ML modelingPythonPyTorchJAXdata preprocessingmodel training

Soft skills

collaborationcommunicationtranslating research ideasdocumentation

Certifications

MSPhD