Jonathan Gu

I build ML systems that turn predictions into product decisions.

At Instacart, I build ML systems that make decisions: bids, rankings, forecasts, and fulfillment actions. My ads bidding system replaced manual product-keyword bids with weekly-budget optimization. It now covers roughly 70% of Sponsored Products spend, about $700M annualized.

The pattern is simple: start with noisy signals, choose the action, measure the result, and keep the loop safe when real users and money are involved. The ads work is reflected in issued patent US 12,511,677 B2.

Outside work, I build Bountiful Garden, OpenClawBrain, and Project Pelican: products about trust, memory, and decisions under uncertainty.

Systems that decide, explain, and hold up in production.
Ads Systems Instacart Work Selected Projects Resume
Production ML

Data, features, models, serving, experiments, rollout, and monitoring.

Decision Science

PhD Economics, UCLA. BA Statistics & Economics, UC Berkeley. Causal thinking applied in production.

Builder Taste

Products that must decide: what to bid, what to remember, whether to act.

At Instacart

Ads, availability, and marketplace decisions at Instacart.

My Instacart work spans ads bidding, ranking, causal marketing measurement, availability prediction, fulfillment, and production ML. A score is not enough. The system has to decide, run reliably, and measure what happened.

What I actually do

I own the full loop: problem framing, data, features, model and control design, serving, experiments, rollout, monitoring, debugging, and migration.

  • Built Instacart's first optimized ads automated bidding system from scratch. It replaced manual product-keyword bids with weekly-budget bidding across roughly 70% of Sponsored Products spend.
  • Started from ROAS equalization: estimate product and keyword returns, choose advertiser target ROAS, pace budgets, simulate bids, write bids, observe outcomes.
  • Built causal marketing measurement and in-app decision systems where observed conversions were not clean labels.
  • Built availability and fulfillment systems across noisy inventory signals, real-time LightGBM serving, calibration, rollout, and monitoring.

Optimized ads bidding

Weekly-budget optimization, ROAS estimation, pacing, bid simulation, and bidding loops for a real ads marketplace.

Causal and marketplace measurement

Coupon and in-app marketing systems where incrementality and selection bias matter.

Availability and fulfillment ML

Models and serving paths for product-store availability, forecasting, calibration, and operational signals.

Selected Projects

Projects that show taste.

These projects share a pattern: turn fuzzy intent into systems that decide, preserve trust, and show their work.

Bountiful Garden neighborhood harvest-sharing interface
Live product Neighborhood software

Bountiful Garden

Bountiful Garden helps neighbors share backyard produce before it goes to waste. The goal is simple: make a local exchange happen.

It is founder-shaped work: product design, trust, and marketplace mechanics for ordinary neighborhood life.

Marketplace Community Full-stack product
Visit Bountiful Garden
OpenClawBrain constellation hero art with a luminous claw and evidence graph
Live product Agent memory v0.2.16

OpenClawBrain

OpenClawBrain gives OpenClaw agents local, inspectable memory. It remembers durable corrections like “use pnpm here” without dumping raw chat history into every prompt.

The spine is simple: SQLite stores the graph, local LLMs propose memories, validators enforce trust boundaries, and proof pages show what was captured or injected. Current public release: 0.2.16.

See OpenClawBrain → How it works Verify it
Project Pelican autonomous options trading research
Private R&D Autonomous trading

Project Pelican

Project Pelican is a private autonomous options research system. It joins data, forecasting, risk controls, execution, and monitoring.

It is still internal. It reflects the same interest: systems that keep making measured decisions under uncertainty.

Quant finance ML pipelines Autonomous systems
Pelican Overview
Patents

Applied ML systems, backed by patents.

My Instacart work spans automated bidding, marketplace decisions, causal targeting, retailer intelligence, and fulfillment optimization.

Broader patent portfolio

Additional filed or submitted work covers double-wide ad auctions, causal targeting, dynamic offers, retailer classification with LLMs, fulfillment optimization, replacement agents, and item display decisions.

Writing

Selected writing.

Writing is where I make the logic visible: what shipped, what failed, and what still seems true after the numbers come in.

Contact

Get in touch.

I am interested in applied ML, agent infrastructure, marketplace design, local software, and systems that make real decisions.

Reach me directly

Email is best for thoughtful notes about projects, collaboration, or roles that fit.

Email LinkedIn Twitter / X GitHub Resume (PDF)