Max Ma
AI Architect at EMPEQ
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolMax Ma is an AI Architect with 11 years of focused ML experience and a 15-year background in enterprise development and consulting, specializing in healthcare, life sciences, and pharma where precision and affordability are paramount. He has led mid-size ML teams and delivered production GxP systems, GenAI platforms, and low-cost MLOps at scale—driving 8 production GenAI systems and 10 PoC/MVPs while emphasizing rigorous scientific discipline and pragmatic engineering. His work spans CV, NLP, GNNs, causal inference and foundation-model applications for genomics, single-cell, and real-world evidence, plus hands-on experience with massive wearable-data pipelines (3 TB/day) from his Microsoft Band days. A pragmatic strategist, Max prioritizes data-centric model boundaries, efficient experiment-at-scale pipelines, and cost-optimized distributed training rather than architecture for its own sake. Notably, his research blends semantic-symbolic pairing and lattice-theoretic ideas for model robustness, and he has contributed CNN and RNN refinements to well-known NLP codebases. Based in Redmond, he combines deep scientific rigor (PhD-trained numerical simulation skills) with startup and enterprise delivery instincts to make AI usable and reliable in regulated domains.
11 years of coding experience
24 years of employment as a software developer
MS/Ph.D, MS/Ph.D at Clarkson University
Github Skills (8)
neural-network10
mask-rcnn10
faster-rcnn10
machine-learning10
rnn-model10
nlp10
python10
n10
Programming languages (2)
Github contributions (5)
neubig/nn4nlp-code
Sep 2017 - Oct 2017
Code Samples from Neural Networks for NLP
Role in this project:
ML Engineer
ML EngineerContributions:10 commits, 4 PRs in 29 days
Contributions summary:Max primarily contributed to implementing and refining Convolutional Neural Network (CNN) models for natural language processing tasks. Their work involved adding CNN classifiers, modifying existing models, and refining the display of activations, indicating a focus on model architecture and analysis. They also worked on Recurrent Neural Networks (RNN) models for sentence modeling, demonstrating a focus on different neural network architectures for NLP. This suggests an active role in designing, training, and evaluating NLP models within the repository.
nlpnatural-language-processingneural-networksmachine-learning
XuezheMax/NeuroNLP
Nov 2016 - Nov 2017
Contributions:183 commits, 185 pushes, 1 branch in 1 year
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