Accelerated Science Intern, Representation Learning for Molecules and Materials

Job description

Job responsibilities

• Implement and evaluate machine learning models for molecules and materials using reproducible training and analysis workflows.
• Design controlled experiments to study how learned representations vary across training runs, model architectures, and learning objectives.
• Investigate the geometry and information content of learned representations and their relationship to physical quantities.
• Develop evaluation methods to distinguish meaningful shared structure from similarities introduced by model parameterization or analysis choices.
• Contribute high-quality, well-documented code to an internal codebase and help translate results into internal reports, publications, and/or patent disclosures (as appropriate).

Job requirements

• Currently enrolled in a doctoral program in materials science, computer science, statistics, applied math, machine learning, physics, or a related discipline.
• Strong foundations in machine learning, linear algebra, probability, and optimization, with demonstrated experience training models on real datasets.
• Familiarity with representation learning or scientific machine learning; experience studying model convergence, learning dynamics, symmetries, or identifiability is a plus.
• Proficiency in Python and modern ML tooling (e.g., PyTorch, experiment tracking, reproducibility best practices).
• Ability to work collaboratively across disciplines and to translate research ideas into working code.