📍 SF Bay Area, CA | 🧭 Principal PM | 🚀 Building full-cycle AI products

I turn human judgment into measurable product quality. After a decade shipping 0→1 and at-scale AI/ML products, I'm now building full-cycle AI tools end to end — from the underlying architecture to the practical apps people actually use.

Most recently Principal PM at Walmart (Data & Ads Global Product Org), where I ran the human-in-the-loop evaluation program behind AI-driven products — designing eval frameworks, scorecards, and annotator operations that made models measurably better.

My background blends Computer Science and an MBA (Cornell University) — technical fluency paired with consulting-grade strategy. I care equally about the low-level architecture and the real-world application.

What I work on

• 🧪 LLM & AI evaluation — eval harnesses, scorecards, quality benchmarks, and human-in-the-loop pipelines
• 🧭 Annotator operations at scale — onboarding, calibration, and quality governance across global vendor teams
• 🎯 Recommendation & personalization — ranking, relevance, and retention-driving ML products
• 🛠️ Full-cycle product building — 0→1 prototypes through scaled launches, hands-on as both planner and builder
• 📊 Quantitative impact — every initiative tied to revenue, retention, quality, or efficiency

I build to learn. Projects are grouped by capability, newest first within each group. Dates are when each was built (→ when it last got real work, not README edits).

Status — four stages of maturity, each one building on the one before:

1. 💡 Concept — a short build made to argue one product position
2. 🛠️ Demo — works end to end, no automated tests yet
3. ✅ Tested — a test suite runs in CI on every change
4. 🚀 Release — packaged as an installable app you can download

Each one picks a narrow problem and takes a position on it. The README in each explains the problem, the positions it defends, and what it deliberately is not.

Four ink-wash animatics from Romance of the Three Kingdoms (三國演義), every scene drawn procedurally in code with a synthesized score, each exporting a real .webm (no stock footage, no AI-generated imagery). Plus one original song, made with a local music model.

• Walmart — Principal PM, Data & Ads · lifted model response quality 42% and helpfulness 38% across 10,000+ enterprise users; scaled eval ops to 600+ annotators across 4 vendor partners
• iHerb — Lead PM · improved recommendation precision 33%, cut churn 22%, raised inter-annotator agreement from 71% → 94%
• Wish — Senior PM · boosted engagement 38% and grew active users 147% on the personalized recommendation engine
• Amazon — Senior PM · lifted forecast accuracy 24% and cut stockouts 18% across millions of SKUs

• Building a full-cycle AI product — owning architecture and application end to end, starting with Throughline for outpatient PT clinics
• Open to Product Manager roles — AI/ML, evaluation, personalization, and platform products
• Sharing what I learn — practical patterns for human-in-the-loop evaluation and AI product development

"Ship measurable quality." — I build evaluation systems and AI products that turn human judgment into outcomes you can prove.