About

Four principles shape how I work.

Speed Through Structure

Designing a development process around rapid, quantified iterations is something I think about constantly. Iteration speed needs to be understood and actively improved. Every iteration needs to move a metric. And metrics need to be meaningful.

That sounds obvious, but in practice it requires being almost obsessive about what actually drives iteration speed: understanding where engineering time goes, what should be more efficient, and taking the steps to make it so.

Good metrics matter as much as good engineering process. The best metrics tell you the truth about where the work actually stands. Without that, the highest iteration velocity will still drive you in the wrong direction.

Ship With Confidence

Proving that a new system is better, or at least no worse, is one of the hardest problems in AI development. At Lyft and Toyota, shipping ML into real-world autonomous vehicles meant every release had to be proven better, or at least no worse, than what came before. I worked at the intersection of ML development, validation, operations, and release to build the processes that got software and ML models onto public roads.

That experience shaped a practical approach to non-regression and impact quantification that applies anywhere AI touches production and mistakes have consequences.

Hands-On, Not Just Advisory

I still build. Not because I have to, but because I want to stay sharp and because I enjoy creating. Alongside client work, I'm a part-time founder shipping a consumer AI app: computer vision for game coaching. Both keep me current with AI-assisted development tools and pressure-test my own frameworks.

When I advise on architecture or process, it's grounded in software I've shipped recently, not slides from years ago.

Drawn to Work That Matters

I'm most energised by teams applying AI to problems worth solving: climate and energy, mobility, biodiversity, and any work where AI makes the humans in the room more capable, not optional. Technology is neutral; where you point it isn't. I enjoy building software that matters.

Credentials

  • Woven by Toyota — Staff Software Engineer. Drove validation, release processes, and ML-first transformation for motion planning. Owned ML data quality across US and UK teams. Two patents: novel approach to autonomous vehicle validation and novel ML data selection paradigm.
  • Lyft Level 5 — Founding team (first 40 engineers). Helped scale the autonomous driving stack from early prototype to public road deployment, an effort that led to a $550M acquisition by Toyota. Built core behavior planning systems and shaped validation and release processes. Recognized for measurable improvements to government-reported safety metrics.
  • Publications — Peer-reviewed work in deep learning and NLP competitions (UPenn SMM4H 2017 Task 2 1st/9, SemEval Sentiment Analysis 2017 9th/37, 2016 17th/34).
  • TUM — M.Sc. Electrical Engineering and Information Technology. Master's thesis on autonomous driving at BMW.

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