I build AI that works.
Co-founder & CTO at Sightline. Ten years of applied machine learning before that. I aim to make a positive impact on society by building AI systems that work in the real world — and I think a lot about where AI goes next.
Now
Sightline is a VC-backed, AI-native supply chain operating system for restaurant chains. As technical co-founder I built the AI/ML vertical from scratch: deep learning forecasting models, an AI recommendation layer that turns predictions into decisions operators actually act on, and the data platform and infrastructure that run all of it in production.
Day to day I'm still an ML/AI engineer: designing model architectures, running training and evaluation, and shipping what works to production. As CTO I also lead the engineering team and scale the product. Sightline is where I'm applying these ideas today; the ideas are bigger than one vertical.
Work
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The AI/ML vertical from zero: forecasting engine, recommendation layer, training and inference pipelines, data platform, cloud infrastructure. Then the engineering team to run it, and now the web app that restaurant chains use every day.
In production for paying enterprise customers.
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My first venture: a "Strava for pilots." ML on flight and geospatial time series for airport and route recommendations, a global airport explorer built on 10M+ points of interest, and a fine-tuned LLM pilot assistant. Designed, built and shipped solo, models on-device via Core ML.
Live on the App Store.
Earlier
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Five years of predictive modeling at scale for Fortune 500 clients: Bayesian marketing-mix models with MCMC inference, time series forecasting, and hundreds of gradient-boosted and neural models on billions of features. Led teams of up to nine data scientists.
Models behind $20M+ in client decisions.
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Recommendation engine for in-flight entertainment, built from passenger viewing history to shape airline media portfolios.
What I believe
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AI is the most powerful technology we have ever built. It is too important to leave to a few.
This technology will touch every human alive today and every one born after us. Everyone must have a seat at the table — in the building, in the decisions, and in the rules — because a future built by a handful is not a future for all.
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Tools expire. Math doesn't.
Every model I've shipped, from Bayesian regression to transformers, came down to the same ideas: distributions, uncertainty, optimization. Learn the tools; invest in the math.
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Trust a model the way a pilot trusts an instrument: only after checking it against the horizon.
A forecast earns trust the way a gauge does — cross-checked against what actually happened, again and again.
About
Paris → California → New York
I grew up in a small town near Paris with one ambition: move to America and build a life from scratch. I earned two Bachelor's degrees at Sorbonne Université, one in Mathematics and one in Computer Science, then moved to California for a Master's in Statistics. I've been in New York since 2019.
Outside of work, I'm a licensed pilot, avid off-the-beaten-path traveler and (unlicensed) French cook at home.
English, French, Spanish.
Writing
Nothing here yet. This is where I'll put occasional writing, long and short, on building ML that works outside the lab and on where AI is heading.
Get in touch
Open to conversations about applied ML, AI research, and what the next generation of AI systems should look like.