Nathan Stanley

“Make Rufus learn from how customers actually use it.”

I took a broad directive to improve Rufus using real customer interactions and turned it into three parallel workstreams: building a secure training environment on AWS, developing training applications using Verl and Megatron-LM for 500B+ parameter models, and curating datasets from real customer traffic. I lead all three efforts and coordinate with science teams to pull it together. Today, a team of scientists uses this framework, infrastructure, and data pipeline to iterate on training recipes for different Rufus applications.

“Prompt changes take too long. Do something about it.”

Tasked with improving prompt engineering velocity, I designed and built an internal platform that mirrors production with the ability to edit prompts deep in the inference stack. The tool provides a full-featured IDE experience for prompt template editing with live evaluation. I led a team of 6 engineers to build it, reached 300+ weekly active users in the first month, and reduced prompt deployment time from 17 days to 3 days. Rufus Studio has since become the canonical platform for all Rufus internal tooling.

“Clubs can't figure out who's causing slow play.”

Hearing that clubs consistently struggled to maintain player speed and identify slowdowns, I invented a novel solution. We tracked players via the scoring app GPS and built a custom algorithm to identify slow players. The frontend resembled a video player over Google Maps where administrators could scrub through any day's timeline and observe player movement. GPS smoothing algorithms provided high-quality signal, and summary reports helped clubs address problematic patterns.

“The legacy system is slow. Make it fast.”

As platform lead, I profiled and optimized MiClub's 2M+ line legacy golf management codebase across the full stack: database engine configuration, query profiling, index optimization, webapp threading, and page load performance. I also built a performance tracker for MiScore to monitor the quality and speed of our OCR scorecard scanning system, reducing scan times from 5 seconds to under 1 second.