Applied AI / machine learning engineering
I’m Daniel, a founder and applied ML engineer. I turn ambiguous questions into testable experiments, inspect where models fail, and build useful systems around what survives.
01 / SELECTED WORK
Recent work starts with an uncomfortable question: how could this result be wrong? I turn that question into a test, build the surrounding system, and keep the failure visible long enough to learn from it.
Gemini can read an invoice, but it should not decide what enters a company’s books. I built SyncABill to extract the invoice, check the result, apply the controller’s review rules, and send approved bills to QuickBooks Online or Xero.
Can an agent fix an ML pipeline without quietly invalidating the experiment? Mendmark turns five realistic failure classes into versioned tasks with hidden graders, reproducible run manifests, and explicit infrastructure outcomes.
A trained, one-output LightGBM model becomes dependency-free Python, C++17, or JavaScript. Executed and compiled tests compare every target against LightGBM raw scores at 1e-12 tolerances.
At Perfsy, I built a computer-vision pipeline to extract structured fields from scanned DMV vehicle titles. I normalized scans with OpenCV and iterated across character primitives, autoencoders, convolutional features, and DNN classifiers.
I operate this vintage-furniture business and use agent-assisted, API-driven workflows for pricing, reporting, seasonality analysis, social media, and Shopify. Earlier models estimated furniture value and classified mid-century pieces, including a fine-tuned CLIP model built before modern VLMs became practical.
02 / HOW I WORK
model.fit().The dataset is messy. The metric lies. Latency enters the room. I like that part: finding the failure, tightening the loop, and turning a promising model into software people can rely on.
Start with a question. Build the dataset, run the experiment, and keep honest notes.
Write the test that could prove the idea wrong. Read the misses, not just the mean.
Give the model dependable data, inference, monitoring, and deployment paths.
Put it in front of a real user and stay close enough to see what breaks.
METHODS + TOOLS
03 / ABOUT
I studied matter at its smallest scales. Then I started companies. Both taught me the same thing: a good answer begins with a better question.
I’m Daniel. I studied condensed matter physics at UC San Diego for roughly three years, then left to build companies and software products.
I have spent the years since moving between equations, code, product decisions, and users. I’m happiest with a hard problem, a fast feedback loop, and teammates who care more about getting it right than looking right.
04 / CONTACT
If you are training or evaluating models, building the systems around them, or turning research into a product, I would like to hear what is hard.