On-Demand ML Infrastructure for a Research Lab

// CASE RECORD

The problem

A bioengineering research group had machine-learning ambitions, Ph.D. candidates writing the code, and no senior engineer in the room. Expensive hardware — Nvidia A100s — sat allocated whether or not it was in use, results were hard to reproduce from one run to the next, and the development practice was whatever each researcher had learned on their own.

What I did

As technical lead, consulting to the principal investigator and the doctoral candidates, I designed the infrastructure for the group’s machine-learning work, with neural-network resource allocation that puts the costly hardware to use on demand rather than by reservation. I engineered and enforced a testable software development lifecycle, ran the code reviews, and met with each engineer regularly to work through their technical problems and the implementation decisions behind them.

The result

Proof-of-concept architectures for scalable ML platforms that use A100-class resources only when the work needs them, reproducibility built into the pipeline rather than hoped for, and a team whose practice improved because someone senior was in the room about two days a month. This is what a standing technical lead looks like on a retainer: architecture, review, and the honest “stop doing that,” for a group that could not justify a full-time hire.