I build ML systems at Meta and spend the rest of my time with a camera or at a nail desk. This is where I keep the work that doesn't fit in a LinkedIn profile.
I've been exploring Bay Area wildlife with a camera. These are picks from a growing field archive, each one a species I wanted to get right.
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I design nail sets the way I'd approach any creative problem. Start with a constraint, build a palette, and refine until every detail earns its place.
I write about the parts of applied ML that aren't about models. Usually it's the interface, the ops, or the part where someone pastes a screenshot of JSON into a chat box.
Take a customer feedback classifier. The model is usually the easy part. The hard part is the Excel paste, the Slack thread, and the screenshot of JSON. This essay compares that workflow built as a traditional Python pipeline and as a Claude Code pipeline, and shows where the real engineering tax lives.
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I'm Travis, a machine learning engineer at Meta based in the Bay Area. My day job is building systems that work at scale. Everything else on this site is what I do because I can't stop making things.
Bird photography taught me patience and attention to detail in ways no debugging session ever could. Nail art taught me that beauty and precision aren't opposites, they're the same muscle. And writing forces me to figure out what I actually believe about the technology I build for a living.
If any of this resonates, or if you're building something interesting, I'd love to hear from you.