Machine Learning Research Group
Research in machine learning focused on information retrieval, application development and modernization, and database performance.
Who We Are
The Machine Learning team focuses on representation learning, language models, and learning on semi-structured data. It provides MongoDB with deep technical knowledge of the latest developments in machine learning.
_Spot.svg)
Research Areas
Representation learning
MongoDB conducts research on enabling embedding models to run on resource-constrained infrastructure and automated fine-tuning approaches.
Learning on semi-structured data
Models that natively support semi-structured data as input are useful for tasks such as supervised learning, DB index recommendations, and cardinality estimation.
Language models for code generation
MongoDB aims to improve the capabilities of language models in the domain of code generation to support application modernization.
Our Team
Research Papers
ORIGAMI: A generative transformer architecture for predictions from semi-structured data
Thomas Rückstieß, Alana Huang and Robin Vujanic, arXiv, 2024ORIGAMI enables efficient end-to-end learning on semi-structured JSON data.
LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations
Robin Vujanic, Thomas Rückstieß, ACL '26, 2026We introduce a knowledge distillation technique that produces embedding models retaining up to 97% of their teacher's performance, while being 5x-15x smaller, 7x-24x faster, and fully compatible with their teacher for flexible deployment.
Read the paper | Models | Blog