Graduate Research Co-op – AI & Representation Learning

Responsibilities

• Support the development of representation learning models to integrate massive-scale hierarchical data with diverse record sets.
• Evaluate and benchmark diverse machine learning models to resolve data conflicts and improve the accuracy of relationship discovery at scale.
• Assist in building scalable ML prototypes that analyze billions of data points to provide automated insights and personalization.
• Develop efficient data pipelines to process and vectorize massive-scale datasets for downstream research and modeling tasks.
• Collaborate closely with senior researchers to document findings and prepare technical reports on model performance and scalability.

• Currently enrolled in a Graduate program (Ph.D. preferred, or MS) in Computer Science, Data Science, or a related quantitative field.
• Solid understanding of Representation Learning, Embedding models, or Large-scale Data Modeling.
• Proficient in Python and modern ML frameworks such as PyTorch or Tensor Flow; familiarity with Vector Databases or Large Language Models (LLMs) is a plus.
• Strong analytical skills with the ability to extract meaningful insights from massive-scale, complex datasets.
• A collaborative spirit and a desire to see research translated into real-world applications.