Maria Lomeli
Research Engineer at Meta
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMaria Lomeli is a Research Engineer in London with 8 years of experience building scalable NLP and ML systems across industry and academia, currently working at Meta. She holds a PhD in Machine Learning from UCL and has moved models from research to production at organizations including Babylon Health and the University of Cambridge. Her hands-on strengths span MLOps and data engineering—highlighted by contributions to Facebook Research’s FAISS, where she implemented and deployed an offline IVF framework with GPU-accelerated search and dimensionality reduction for large-scale vector similarity. Maria combines deep probabilistic and actuarial training with practical engineering, enabling robust, efficient solutions for high-volume inference workloads. Colleagues rely on her to bridge rigorous evaluation and production performance tuning in NLP pipelines.
9 years of coding experience
8 years of employment as a software developer
Universidad Nacional Autónoma de México (UNAM)Bachelor's degree Actuarial Science, Bachelor's degree Actuarial Science at Instituto Tecnológico Autónomo de MéxicoUniversity College London
Github Skills (9)
machine-learning10
faiss10
python10
dimensionality-reduction10
gpu-programming9
pytorch8
data-engineering8
cicd7
testing7
Programming languages (4)
Github contributions (5)
facebookresearch/faiss
Nov 2022 - Nov 2022
A library for efficient similarity search and clustering of dense vectors.
Role in this project:
MLOps Engineer & Data Engineer
MLOps Engineer & Data EngineerContributions:3 releases, 7 commits, 20 PRs in 6 days
Contributions summary:Maria primarily contributed to the development and deployment of the offline IVF framework, which leverages big batch search for efficient vector similarity search on large datasets. Their work involved integrating the framework with GPU-accelerated FAISS for significant performance gains. This included implementing and testing dimensional reduction techniques, demonstrating a focus on scalable and optimized machine learning solutions. The user also worked on test and evaluation of the offline IVF framework.
clusteringsimilarity-search
mlomeli1/faiss
Nov 2022 - Jan 2025
A library for efficient similarity search and clustering of dense vectors.
Contributions:28 pushes, 9 branches in 2 years 2 months
clusteringsimilarity-search
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.