Summary
Michael Suguitan is a robotics software engineer with 11 years of experience building ML-driven robot behaviors, telepresence systems, and MLOps tooling across academia and industry. He completed a PhD at Cornell where he designed and open-sourced Blossom, a telepresence robot platform, and led multimodal neural network research for generative robot behavior. Michael has translated that research into production-focused roles—from training large models on multi-GPU clusters at ABB to shipping experiment-tracking and API-driven MLOps at modlee. He combines mechatronics and software skills (PyTorch, TensorFlow, AWS/GCP, vector DBs) with rigorous user studies and statistical analysis to validate human-robot interaction designs. Currently based in New York, he is building robotics software at Fauna Robotics while maintaining a curiosity-driven practice (e.g., a self-directed Recurse Center term) that bridges creative technologies, HCI, and ML. An engineer who moves seamlessly between hardware control and large-model ML, he focuses on making robot behavior both technically robust and human-centered.
11 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree Mechanical Engineering Minor in Computer Programming, Bachelor’s Degree Mechanical Engineering Minor in Computer Programming at North Carolina State University
Doctor of Philosophy (Ph.D.) Mechanical Engineering, Doctor of Philosophy (Ph.D.) Mechanical Engineering at Cornell University
English, Tagalog, Japanese