Miguel Alonso is a Senior Member of the Technical Staff with 20+ years blending machine learning, AI, and software engineering to deliver production-grade systems in computer vision, robotics, AR, energy, and autonomous systems. He bridges research and product delivery—leading teams of 5–30 through requirements, design, and CI/CD-driven implementation while remaining hands-on in Python, C/C++, C#, and Java. His recent work spans building full-body teleoperation pipelines, simulation-driven sim-to-real transfer for humanoids, and LLM-driven NPC tooling at Unity, where he also maintained the widely used Unity ML-Agents open-source toolkit. An academic entrepreneur as a former associate professor and center director, he has repeatedly converted research into deployed products and grant-funded projects. Based in Miami, he combines deep reinforcement learning and optimal control expertise with a practical knack for documentation and tooling—evident from his contributions improving ML-Agents API docs and automation for reproducible docs generation.
11 years of coding experience
25 years of employment as a software developer
Ph. D. Electrical and Computer Engineering - Image Processing Computer Vision Intelligent Control, Ph. D. Electrical and Computer Engineering - Image Processing Computer Vision Intelligent Control at Florida International University
Lean LaunchPad Educator Entrepreneurship/Entrepreneurial Studies, Lean LaunchPad Educator Entrepreneurship/Entrepreneurial Studies at National Collegiate Inventors and Innovators Alliance
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Role in this project:
Technical Writer & Documentation Specialist
Contributions:5 releases, 94 reviews, 165 commits in 1 year 10 months
Contributions summary:Miguel primarily focused on updating and expanding the API documentation for the ML-Agents toolkit, including adding Python Low Level API documentation. They made edits to the documentation, fixed docstring issues, and implemented a pre-commit hook to automatically generate markdown documentation using `pydoc-markdown`. The user also updated the docs to set epsilon to be linear by default. They also added documentation for new features like the training area replicator.
A general purpose library for training any type of GPT model.
Contributions:1 release, 15 reviews, 18 PRs in 1 year 9 months
gpt
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