Software engineer focused on reliable AI systems, backend infrastructure, and full-stack products.
I'm based in San Francisco. My work spans healthcare and fintech, with a focus on making AI behavior measurable and production failures easier to diagnose.
Previously, I was a founding engineer at Double Blind Bio, working on a clinical-trial AI product, evaluation tooling, observability, and backend reliability. Before that, at Baselayer, I built business-verification services, risk-scoring workflows, and agent-assisted data enrichment.
llm-reliability-lab is an offline Python evaluation harness for structured invoice extraction. It demonstrates how a schema-valid change can corrupt an invoice identifier, how field-level metrics and critical-error gates catch it, and how a baseline comparison verifies the repair.
It uses synthetic documents and authored predictions. It demonstrates evaluation mechanics; it does not report live-model accuracy or production results.
Quickstart · Walkthrough · Example reports
- Photoreceptor Eye Model: Python and OpenCV experiments in afterimage simulation and image/video processing.
- NLP API: a small FastAPI and Hugging Face prototype exposing sentiment analysis and text-generation endpoints.
- ADHD Assistant: an archived Next.js and TypeScript prototype for task planning, habits, and focus sessions.
Python · TypeScript · SQL
FastAPI · Node.js · Next.js · React · PostgreSQL / pgvector
LLM evaluation · LangGraph · DSPy · Docker · GCP · observability
I graduated from UC Berkeley with a B.A. in Computer Science, Data Science, and Economics.
Open to software engineering roles in applied AI, backend, and full-stack product development.



