Sensing systems · model-serving evaluation · scientific Python
I build ML and data systems for sensors, latency constraints, and messy measurements.
My work spans quantum-sensing pipelines, calibration tools, scientific Python, and model-serving benchmarks. I like problems where the hard part is getting the measurement right.
Seeking research-engineering roles on teams building sensing, scientific ML, robotics, or low-latency model-serving systems.
Three kinds of systems I build.
Grouped by capability rather than repo count: sensing and calibration systems, model-serving evaluation, and research infrastructure that keeps claims traceable.
Software for sensing data, calibration, and model evaluation.
Previously at Dirac Labs, I worked on quantum sensing systems: signal processing, calibration, geophysical analysis, field and aerial data workflows, and real-time inference pipelines for magnetic navigation and detection.
I build software around noisy measurements: sensing pipelines, calibration workflows, model evaluation, and hardware-adjacent data systems.
Today I am focused on reliable model-serving evaluation and intelligent systems that have to work with sensors, calibration drift, field data, or latency constraints.
Additional public artifacts.
The clusters above are the main hiring signal; these repos are framed by what I was trying to learn, what I built, what the work revealed, and where the artifact honestly stands.
Questions I am actively working through.
If you are thinking about one of these, I would probably enjoy comparing notes.
Interested in sensing systems, model-serving evaluation, or research engineering?
Reach out if you are working on sensing data, model-serving evaluation, calibration-heavy ML, or scientific software that needs careful measurement.