sr_tpm · portfolio_lead · ml_infrastructure
Twenty‑five years shipping ambitious, high‑stakes programs at the world’s most demanding engineering organizations. Currently leading ML infrastructure reliability — as a Portfolio Lead — for GPU and TPU fleets at Google Cloud.
01 · about
Compute program leader who keeps hyperscale GPU/TPU AI fleets reliable, efficient, and online. 25 years across Google Cloud, AWS, Microsoft, and BlackBerry owning the full compute lifecycle — capacity bring‑up, fleet reliability and utilization, hardware lifecycle, and cost efficiency — with the fleet‑wide observability that gives leadership a single source of truth on supply, demand, and health. At my best owning ambiguous, high‑stakes problems end to end: leading crisis response across software, hardware, manufacturing, and supply chain, and turning it into repeatable systems. Cumulative impact to date: $1B+ in savings, cost avoidance, and downtime mitigation.
02 · why ai
AI tools changed how I work — and that changed what I want to work on. I use Claude, Gemini, and ChatGPT daily, not as productivity shortcuts but as genuine thinking partners. Working on the infrastructure that makes these models possible, I’ve watched the technology move from impressive to transformative faster than any platform shift I’ve seen in twenty‑five years.
What makes AI infrastructure uniquely demanding isn’t the hype. It’s the specific, hard problem: how do you ship the most capable AI systems ever built — safely, reliably, at speed, at scale? That is a program management problem. The stakes are higher, the cross‑org complexity is greater, and the cost of getting the design‑vs‑execution tradeoff wrong is more consequential than anything I’ve encountered before.
My career has been a series of platform‑defining bets — BlackBerry’s global supply chain, AWS compute at pandemic scale, Surface at $50M development budgets, and now ML infrastructure for Google’s GPU and TPU fleets. Frontier AI is the next platform. I want to help build the operational foundation that lets the best researchers in the world do their best work — safely.
I’ve started building with these tools too — a portfolio app that consolidates my investments across multiple institutions into a single performance view, and a Python optimization add‑on for Google Sheets. Not because I’m becoming an engineer, but because the gap between “I have an idea” and “I have a working tool” has collapsed. That changes how I think about what’s possible inside a program.
03 · philosophy
Every program lives on a single dial: how much time you spend designing versus how much you spend executing.
Get that ratio wrong in either direction and the program fails — over‑designed programs miss the window, under‑designed ones burn cash rediscovering what a week of thinking would have surfaced.
The right ratio is not a constant. It moves with the cost of being wrong, the reversibility of the decision, and the rate at which the surrounding context is changing. A flagship hardware launch with a fixed factory window deserves more design up front than a software experiment whose first release teaches you more than any document could.
In AI development, this problem becomes asymmetric. Moving too slowly cedes the frontier. Moving without enough design produces reliability and safety failures that compound in ways that are hard to recover from. The dial matters more here than anywhere I’ve worked — and calibrating it correctly, in real time, at speed, is the whole job.
Everything else I do follows from that: how I structure milestones, how I escalate, how I trade scope, how I decide when to commit. Programs do not fail because people are unskilled. They fail because someone optimized the wrong side of that dial.
04 · experience
Senior Software Technical Program Manager & Portfolio Lead — ML Infrastructure
Senior Supply Chain Program Manager — AWS Infrastructure New Product Launches
Senior Technical Program Manager — Surface
Program Manager — NPI, Manufacturing, Supply Chain
NPD Engineer · General Manager
Created a $100M caps‑and‑closures product line the industry had pursued for two decades. Four U.S. patents and a peer‑reviewed engineering paper emerged from this chapter.
05 · selected impact
$1.03B+
Own a 9‑program portfolio across software, hardware, manufacturing, and supply chain — delivering cumulative savings, cost avoidance, and downtime mitigation across custom AI compute fleets.
google cloud · ml infra$761M
Shifted servicing from full‑tray decommissioning to component‑level board replacement, cutting tray‑swap rate 37.3% → 4.7% — 4,800+ swaps avoided in year one.
google cloud · hardware58% faster
Modular maintenance model reduced new‑cluster bring‑up from 34 to 14 days — accelerating delivery of new AI infrastructure capacity at Google Cloud scale.
google cloud · gpu/tpu84% faster
Fleet‑wide diagnostic pipeline cut scan time from 6.3 days to under 24 hrs, enabling faster fault isolation across the AI accelerator fleet.
google cloud · reliability$1.5B protected
Led fleet‑wide AI‑chip overheating crisis: VP war‑room, dashboard governance, MTTR under 2 days across 1,600+ chips.
google cloud · crisis$65M saved
Zero‑downtime hot‑swap program enabling 85% repair coverage without draining compute capacity from live AI workloads.
google cloud · ml infra06 · skills
AI & daily tools
Data & analytics
Program leadership
Domains
07 · patents & publications
Four issued U.S. patents and two published works from an earlier chapter — one in engineering, one in leadership thinking. They matter here because the program instinct is grounded in knowing how things actually get built, and in writing clearly about why they should be built that way.
US 20100278962 ↗
US 20080317898 ↗
US 20100272849 ↗
US 20100227019 ↗
08 · contact
Send a message directly, or connect on LinkedIn or X. I read everything.
kirkland, washington