Accelerated Science Intern, Representation Learning for Molecules and Materials

Location: Hybrid, in-office role in Los Altos, California, United States

Salary: $45–$65/hour for California-based roles

Company

hirify.global develops tools and capabilities in AI, robotics, driving, and material sciences to improve human mobility and quality of life.

What you will do

• Implement and evaluate machine learning models for molecules and materials using reproducible training and analysis workflows.
• Design controlled experiments 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 effects of model parameterization or analysis choices.
• Contribute documented code and translate results into internal reports, publications, or patent disclosures as appropriate.

Requirements

• Currently enrolled in a doctoral program in materials science, computer science, statistics, applied mathematics, 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.
• Proficiency in Python and modern machine learning tooling, including tools such as PyTorch, experiment tracking, and reproducibility practices.
• Ability to collaborate across disciplines and translate research ideas into working code.

Nice to have

• Familiarity with representation learning or scientific machine learning.
• Experience studying model convergence, learning dynamics, symmetries, or identifiability.

Culture & Benefits

• Paid 12-week summer internship with a hybrid, in-office format.
• Collaboration with TRI researchers, universities, national laboratories, and colleagues across global Toyota.
• Medical, dental, and vision insurance.
• Paid time off, including holiday pay and sick time.