Summary
PM for Meta's real-time metrics and analytics warehouse in the AI & Data Infrastructure org. Scaled to absorb 9x agentic-workload traffic in 90 days while tightening latency and freshness SLAs. 20 years and three acquisitions (Supernatural to Meta, Elevate Security to Mimecast, Habitry to Lift The Bar). Co-inventor on patent US12149553B1, an ML risk-scoring and intervention system for social-engineering attacks at Fortune 500 customers.
Employment
Meta
2023.03 – Present
2023 – Present
Product Manager, AI & Data Infrastructure
- Scaled Meta's real-time metrics and analytics warehouse to absorb 9x agentic-workload traffic in 90 days while tightening latency and freshness SLAs.
- Drove alignment and shipped a novel OLAP with watermark-based consistency SLOs; real-time freshness, <100ms latency, 100M QPS, 10x capacity savings.
- Led Supernatural's data and dev infrastructure migration onto Meta following acquisition.
Supernatural (acquired by Meta)
2022.09 – 2023.03
2022 – 2023
VP of Product
- Led a 17-person product organization (PMs, UXR, Program Managers) through 3x revenue and member-base growth, then through acquisition by Meta.
- Guided technical development of real-time multiplayer infrastructure for VR fitness.
Elevate Security (acquired by Mimecast)
2020.01 – 2022.09
2020 – 2022
VP of Product
- First product hire; built the team from scratch (PMs, POs, Designers, Technical Writers).
- Co-inventor on patent US12149553B1, an ML risk-scoring and intervention system for social-engineering attacks.
- Led product pivot that drove Fortune 500 adoption, 2x ARR, $8.25M Series A, and Mimecast acquisition.
Tonal
2018.11 – 2020.01
2018 – 2020
Director of Product Management, Engagement
- Coordinated decision metrics across hardware, firmware, software, and content; 2x engagement, 80+ NPS.
- Developed content strategy and workflows that increased production output 27x.
Habitry (acquired by Lift The Bar)
2013.08 – 2018.11
2013 – 2018
CEO, Head of Product, Co-Founder
- Co-founded, raised $150K, shipped the first behavior-change iOS app for health coaches; sold.
Flowstate
https://github.com/smledbetter/flowstate
Observability platform for AI agent development. Tracks metrics, traces, gate-failure signals, drift detection.
- Sustained 97% prompt-cache hit rate across 130 sprints in 16 projects. Only ~3% of per-sprint tokens are new work, enabling a 842M-token single-operator agent-dev pipeline.
- Pre-registered factorial experiments; killed 2 of 3 proposed workflow changes after measuring a 10–13% noise floor.
Jig
https://github.com/smledbetter/jig
Build-time eval governance for LLM apps. No production-signal registered, no compile.
- Closes the offline/online eval gap behind high-profile AI walkbacks (Klarna, 2025): features pass eval on green offline numbers, then regress in production.
- Deterministic judges keep LLMs out of the audit path. Slots above existing eval frameworks (Braintrust, Promptfoo, Inspect AI) as a governance layer, not a replacement.
- MIT, PyPI as jig-eval. 249 tests, 94% coverage. Submitted to UK AISI's Inspect AI (issue #3770).
Gas Town
https://github.com/gastownhall/gastown
Open-source multi-agent workspace manager for federated LLM task execution.
- Identified a security vulnerability in federated completions where tool-call confabulation propagated fabricated data to downstream agents in 82% of trials.
- Proposed and designed HMAC-signed receipt gates based on original research (Receipt-Gated Pipelines), reducing confabulation propagation to 0% across 686 trials.
Skills
Product Leadership — three acquisitions, patent co-invention, infra org transitions
Data Infrastructure — DuckDB, OLAP, real-time analytics, telemetry pipelines
AI & LLM Agents — Anthropic SDK, Claude Code, MCP, eval design, prompt caching, tool-call verification
Machine Learning — LLM-as-judge, drift detection, factorial design, noise-floor calibration
Education
Masters, Sport Psychology — John F. Kennedy University (2014)
Bachelors, Philosophy — University of Chicago (2004)