Jiquan Ngiam
Co-Founder, CEO at MintMCP
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolJiquan Ngiam is a founder and engineering leader with 14 years of experience building production-grade AI systems and developer platforms from startup to Google Brain. As Co-Founder and CEO of MintMCP, he is focused on making AI enterprise-ready with observability, guardrails, SSO, RBAC and auditable trails, drawing on deep product and systems instincts honed at Coursera and Google. He spent four years as a Senior Staff Engineer on Google Brain and has an ongoing history of hands-on ML framework work, contributing to TensorFlow Lingvo to improve language models and testing infrastructure. Trained at Stanford (PhD) and Carnegie Mellon (BS), he blends rigorous research pedigree with practical delivery, having been Coursera’s first engineering hire and built teams from the ground up. Colleagues would describe him as a pragmatic technologist who moves quickly between strategy, architecture and code, with a quietly consistent record of turning cutting-edge ML into reliable, auditable products.
14 years of coding experience
5 years of employment as a software developer
B.S. Computer Science, B.S. Computer Science at Carnegie Mellon UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Stanford University
Github Skills (12)
nlp10
language-model10
tensorflow10
python10
natural-language-processing10
accelerated-computing9
gpgpu9
gpu9
machine-translation8
distributed-computing7
speech-recognition7
speech-synthesis7
Programming languages (4)
Github contributions (5)
tensorflow/lingvo
May 2019 - Oct 2021
Lingvo
Role in this project:
ML Engineer
ML EngineerContributions:58 commits, 1 comment in 2 years 5 months
Contributions summary:Jiquan primarily contributes to the TensorFlow Lingvo framework, focusing on improvements and extensions for various machine learning models. The commits demonstrate the user's involvement in implementing and refining model components, including language models, and also in improving the integration of the models to different layers. The user frequently modifies the testing infrastructure to accommodate different model types and configurations.
lingvospeech-recognitiontranslationspeech-to-textmachine-translation
d8e-ai/lutra-plugin
Jan 2024 - Feb 2025
Contributions:58 reviews, 1 PR, 4 pushes in 1 year 1 month
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