Hello, I'm Justin

Software & ML Engineer | PhD Economist

I build software and machine learning systems for technically demanding problems and real-world data. Most recent work has been in two areas:

Scientific & industrial ML — measurement data, scientific instruments, and sensor systems: spectroscopy, imaging, sensor fusion, detection, and simulation pipelines, where the data comes from the physical world.

Edge & privacy-preserving AI — fine-tuned small models running locally, sometimes on embedded or constrained hardware, and deployments where data can’t leave the device, network, or jurisdiction.

I’m comfortable across a range of work spanning from embedded firmware to large-scale compute pipelines, modeling, and production engineering. I’ve also modernized monolithic legacy C into clean, modular, and deployable code. I’m used to working alongside domain experts who own the underlying science.

There’s also a non-engineering side to what I do. As a trained economist, I read and think seriously about the information environment, finance, and public policy — for some problems, that’s the relevant expertise, not code.

Available for new engagements (as of May 15)

I work independently, mostly with US clients (through a US LLC for their convenience), on engagements from a few weeks to about a year. If you have a problem that sits at one of these intersections and isn’t quite anyone’s standard job description, that’s often a good fit. Reach out at justin@justinbriggs.net.

Background

Two decades developing software and crunching numbers as an engineer and research scientist for Leidos and SAIC, on programs for DARPA, IARPA, DHS, and the Army. Using ML since about 2010. B.S. Computer Science and Math; Ph.D. in Economics — empirical research using NLP and large-scale media data, especially useful for work needing careful statistical reasoning alongside the engineering. See LinkedIn for more.

Hardly relevant

Outside of work, much of my time now goes to raising my kids. But my interests are diverse. I’ve enjoyed cycling (and bike building), soccer, woodworking, backpacking, scuba diving, brewing beer, gymnastics, singing, rock climbing, and photography. I’ve also travelled widely, on six continents and to 49 states.

In college, alongside Math and CS, I took courses ranging from Physics and Astronomy to Philosophy, Psychology, Modern Dance, and Death & Dying. I learned Economics, French, and most of my Spanish after finishing undergrad.

My PhD was empirical and focused on the Media, with concentrations in monetary, development, industrial organization, public choice, and law and economics.

Lately I spend a lot of thought on the information environment and what it would take for truth and reason to win out, on the effects of AI on society, and on markets and the more prosaic matter of how to invest well.