Mauricio Tec
Boston, Massachusetts, United States
Summary
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SeniorMauricio Tec is a Senior Applied Scientist specializing in reinforcement learning, LLMs, and diffusion generative models, with 12 years of industry experience and a PhD-level background in machine learning and statistics. He has led interdisciplinary teams at Harvard, securing $1.5M in grants and publishing in top-tier venues while building RL-driven agent frameworks that improve interpretability and resource allocation. Currently at Amazon, he focuses on post-training RL to optimize agentic capabilities of multi-modal frontier LLMs and scaling RL infrastructure for billion-parameter models. His work spans practical production deployments—from portfolio optimization systems in finance to robustness testing on edge hardware—demonstrating a rare blend of academic rigor and engineering impact. Mauricio also explores novel transfer learning for discrete diffusion LLMs and online RLHF techniques that make weak LLM judges more useful, hinting at a deep interest in actionable, deployable AI. Based in Boston, he welcomes collaborations that bridge research breakthroughs with real-world systems.
12 years of coding experience
Stackoverflow
73reputation
496reached
0answers
1question
Github Skills (53)
simulation9
julia8
del8
causal-inference8
las7
science7
html6
database6
hpc6
machine-learning6
javascript6
programming-language6
r6
snappy5
vscode5
Programming languages (9)
Github contributions (5)
ITAM-DS/estadisticaComputacional2016
Oct 2016 - Nov 2016
Contributions:40 commits, 41 pushes, 1 branch in 1 month
itamdelcursolas
mauriciogtec/rule-bottleneck-reinforcement-learning
Dec 2024 - Aug 2025
Code for paper: Rule-Bottleneck RL: Joint Decision and Explanation for Sequential Resource Allocation with LLM Agents
Contributions:5 reviews, 70 PRs, 166 pushes in 7 months
llm
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