We’re a small, stealth-stage medical device startup in California building a novel magnetocardiography (MCG) device, a noninvasive way to read the heart’s magnetic signals for earlier cardiac diagnostics. Founded by a former UCLA professor (science), a Wharton MBA / ex-J.P. Morgan VP (business) and a renowned physician (medicine).

We’re looking for a remote, part-time coder to help build a repeatable simulation package as we work toward FDA pre-submission and de-risking. Working with AI tooling, you’d produce:

•	Simulation scenarios
•	Synthetic MCG-like data
•	Plots and SNR metrics
•	Assumptions logs and clean, reviewable outputs

How it works: you generate the material; our two founders review it, one for scientific plausibility (source model, sensor assumptions, plots), the other for regulatory fit (claim scope, physician interpretation, risk-benefit, predicate/comparator thinking, pilot-study planning). The goal is credible, well-documented evidence that guides the next de-risking step.

Good fit if you have: strong Python, comfort with data simulation / signal processing and plotting, and ideally a biomedical or health-data background. Biomedical computation and informatics folks especially encouraged to reach out.

Compensation: equity only at this stage (no cash), terms to be discussed, a genuine get-in-early opportunity rather than a paycheck. Once funding is completed this could turn into a full-time role which would of course be cash-compensated.

Location: fully remote, part-time.

Interested? Reply with a short note on your background and a link to GitHub or a portfolio if you have one.