PhysioNet Competitor (Biomedical Signal Processing & ML)

Biomedical signal work

Develop advanced technical problems that help improve AI reasoning about physiological signals, time series and machine learning. The work draws on scientific programming and the ability to turn noisy data into a rigorous, reproducible analysis.

Prior AI training project experience is not required. You should be able to explain the methods and experiments you developed, the technical decisions you owned and how you validated the results.

• Create self-contained problems involving biomedical signals, signal processing and machine learning.
• Develop clear reference solutions using Python, MATLAB or another appropriate scientific computing environment.
• Work on preprocessing, filtering, segmentation, feature extraction, representation learning, classification or forecasting tasks.

Preferred experience

Participation in a PhysioNet/Computing in Cardiology Challenge or a comparable biomedical or scientific machine learning competition is preferred. Strong results should be supported by evidence of a substantive personal technical contribution.

Public evidence may include an official team result, conference paper, technical report, open-source repository or meaningful code contribution. Practical experience with ECG, EEG, PPG, EMG, respiratory signals, polysomnography or other clinical time series is relevant.

Schedule and compensation

This is a worldwide remote contractor opportunity for approximately 15 hours per week. You choose your hours and days, including weekends if desired.

The listing states $60 to $120 per hour, while compensation is paid for each task that meets project specifications. Completion time varies with experience and workflow, and minimum submission requirements apply.

Selection and start

Selection includes screening questions, an approximately 30 minute AI interview and hiring manager review. Roles are typically filled within 48 hours; selected contributors are expected to begin their first task within 24 to 48 hours after onboarding.