LLM training data & evaluation
May 1, 2025
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1 min read
At Outlier, I worked as an AI Software Engineer on programs that supply high-quality training and evaluation data for large language models.
Focus areas
- Prompt-driven workflows that stay reproducible and easy to audit.
- Analysis passes that catch inconsistencies before datasets ship downstream.
- Communication loops with subject-matter experts so nuanced domains stay faithful in the data.
Why it matters
Great models depend on great data. This role reinforced a principle I carry into robotics and coursework: measure twice, document assumptions, and treat labeling and evaluation as engineering disciplines—not afterthoughts.
Authors
CSE & Math @ OSU · Robot Operator @ Diligent Robotics
Computer Science and Engineering student at The Ohio State University focused on machine learning and systems programming in Python and C++. I care about shipping reliable software—from deep reinforcement learning experiments to real-world robotics in hospitals—and about learning from every layer of the stack.