Instacart Ads Automated Bidding
Budget-based marketplace ads bidding, return estimation, pacing, simulation, and production rollout.
PhD Economics, UCLA ยท Senior machine learning engineer
Economics, machine learning, and decision systems.
I build production systems where a model has to make a real decision, leave enough evidence to audit it, and improve without outrunning its guardrails.
Work
The production marketplace systems come first. The independent projects carry the same questions into agent memory, experimentation, local coordination, and policy.
Budget-based marketplace ads bidding, return estimation, pacing, simulation, and production rollout.
Causal targeting from experiment outcomes: expected incremental effect, budget allocation, and policy evaluation.
Item-store availability and fulfillment ML with noisy catalog, store, shopper, and operational signals.
One local, source-backed brain for ChatGPT/Codex, Claude Code, and OpenClaw. It compiles shared history into scoped memory, learns from outcomes, and builds a private training corpus that has to pass a blind evaluation before it earns a model claim.
Neighborhood produce-sharing marketplace: product, trust, local coordination, and full-stack execution.
A working book about the system between generation and allocation: how to propose safely, assign traffic, log propensities, join reward, update policy, and decide what is worth learning next.
A collaboration system for turning ambiguous work into inspectable plans, evidence, and clean handoffs without hiding the reasoning that produced them.
A retrieval-first policy assistant that keeps source documents, jurisdiction, and privileged operations visible instead of answering from a generic policy-shaped memory.
An inspectable forecasting and decision dashboard built around calibration, uncertainty, and the difference between a model score and a decision worth taking.
Links
Straight to the useful stuff.