Science Intern, Representation Learning for Molecules and Materials

Tri-global Solutions Group Inc.

. Implement and evaluate machine learning models for molecules and materials using reproducible training and analysis workflows.

Posted 10/30/2026internshipLos Altos • California • United StatesEntry Level💰 $45 - $65 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning model implementation and evaluation, with strong foundations in linear algebra, probability, and optimization. Proficient in Python and modern ML tools, capable of translating complex research into practical applications.

Highest-signal resume keywords

Machine LearningPython ProgrammingModel EvaluationRepresentation LearningCollaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningLinear AlgebraProbabilityOptimizationModel TrainingExperiment DesignData AnalysisModel ConvergenceLearning DynamicsIdentifiability

Soft Skills

CollaborationCommunication

Tools & Technologies

PythonPyTorchExperiment TrackingReproducibility Best Practices

Industry Keywords

Materials ScienceComputer ScienceStatisticsApplied MathScientific Machine Learning

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

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.
Help translate results into internal reports, publications, and/or patent disclosures, as appropriate.
Collaborate with TRI researchers to investigate how training data, learning objectives, and model architectures shape learned representations.

Requirements

What you’ll need

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, and reproducibility best practices.
Ability to work collaboratively across disciplines and translate research ideas into working code.

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Paid time off benefits, including holiday pay and sick time