Applied Scientist & Machine Learning Engineer

      [Projects](<https://ehildebrandtrojo.notion.site/Projects-30f33dbe3720805b9efcd5acf1ceab2c>)          |          [Research & Writing](<https://ehildebrandtrojo.notion.site/Edgar-Hildebrandt-Rojo-2f26d5bfaabe4c18b7a303659e563253>)          |          [Resume (PDF)](<https://ehildebrandtrojo.notion.site/30b33dbe37208061b18fffdb33d96035>)          |          [Contact](<https://ehildebrandtrojo.notion.site/30b33dbe37208061b18fffdb33d96035>)          

I build large-scale decision systems at the intersection of reinforcement learning, simulation, and LLM evaluation.

How do we design AI systems that make reliable decisions under uncertainty?

At Johns Hopkins Applied Physics Laboratory, I design and deploy simulation and machine learning infrastructure for autonomy programs supporting the US Navy and Coast Guard. My core focus includes:

• Distributed Simulation: Architecting and scaling Monte Carlo pipelines to 1,000,000+ episodes on HPC clusters.
• Reinforcement Learning: Constructing environments that integrate real-time system state into closed-loop training for autonomous agents.
• LLM Evaluation: Developing domain-specific evaluation frameworks and schema compliance protocols to measure hallucination and logical robustness.

Rather than focusing solely on model training, I specialize in the systems around the model — the environments, reward structures, and architectural constraints that determine whether an AI system survives contact with reality.

My approach is grounded in a triple major in EECS, Applied Mathematics, and Economics from University of California, Berkeley, followed by an M.S. in Artificial Intelligence from Johns Hopkins University. This blend of systems engineering, statistical rigor, and economic reasoning informs how I build practical, high-agency AI systems.

I’m interested in joining high-autonomy teams building AI systems that reason, plan, and optimize within complex physical and economic environments.

Highlighted Projects

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💼 An Empirical Deep Dive into the Past 3 Years of Tech Layoffs

Data Science, Web Scraping, Visualization Jan 2023

Analyzed Covid-19 and inflation-caused mass layoffs in tech industries, comparing company sizes and Key Performance Indicators. While Covid-19 tech layoffs affected early-stage and IPO companies, recent layoffs affect more IPOs [Link]

Developed using Selenium, Pandas, Statsmodels, and Seaborn

An Empirical Deep Dive into the Past 3 Years of Tech Layoffs

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🔍  Stock Search

Front-end, Back-end, Visualization, Startup Idea Jun 2022 — Present

Stock Search is a website which displays historical stock price data at 30 min intervals for 450+ companies for the past several months, going beyond what Google & Yahoo offer [Link]

Developed using Svelte, SvelteKit, Chart.js, Tailwind CSS, DaisyUI, MongoDB, and Vercel