Namit Katariya
Research Engineer at Mistral AI
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolNamit Katariya is a research engineer and senior ML leader with 14 years of experience building production-grade machine learning systems across startups and large tech companies from Palo Alto. He combines research rigour with hands-on infra skills—shipping clinical dialogue summarizers, prescription recommendation systems, and end-to-end patient care simulators at Curai, and optimizing large-scale Spark-based scoring pipelines and open-sourcing tools like ScANNS and contributions to photon-ml at LinkedIn. As a manager and tech lead he has hired and grown MLE teams, defined ML platform strategies, and moved metrics-driven automation from prototype to 80%+ success in deployed workflows. Now at Mistral AI after leading Faire’s AI platform, he focuses on bridging cutting-edge research and robust, scalable ML infrastructure—often surfacing practical solutions that reduce runtime from days to hours.
14 years of coding experience
12 years of employment as a software developer
Indian Institute of Technology BombayMinor Statistics, Minor Statistics at Department of Statistics, IIT BombayMasters degree Computer Science, Masters degree Computer Science at Carnegie Mellon University
Github Skills (11)
apache-spark10
machine-learning10
trainings10
data-validation10
modeling10
scala10
generalized-linear-models9
glm9
data-processing9
testing9
lm9
Programming languages (3)
Github contributions (5)
linkedin/photon-ml
Jan 2016 - Jul 2017
A scalable machine learning library on Apache Spark
Role in this project:
ML Engineer
ML EngineerContributions:26 commits, 10 PRs, 6 pushes in 1 year 6 months
Contributions summary:Namit primarily focused on enhancements and bug fixes within the `photon-ml` library, a machine learning library on Apache Spark. Their work involved modifying core components like the `ModelTracker` case class, as well as integrating selected feature files within the codebase, demonstrating a deep understanding of the internal workings of the library. Furthermore, the user contributed to data validation and testing procedures, suggesting an emphasis on the reliability and robustness of the ML pipeline. These contributions reflect a strong involvement in the model training and data processing stages of the library.
scalabledata-sciencemachine-learningapachespark
namitk/ml-with-large-datasets
Feb 2013 - May 2013
Contributions:10 commits in 3 months
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