The work
You will create self-contained technical problems involving biomedical signals, then develop reference solutions and criteria for evaluating submitted answers. Day-to-day tasks may cover preprocessing, filtering, segmentation, feature extraction, representation learning, classification or forecasting across ECG, EEG, PPG, EMG, respiratory signals, polysomnography and other clinical time-series data.
Technical expectations
The platform screens for direct work with noisy physiological data, substantive ownership of competition methods and the ability to explain experimental decisions under follow-up. You should be prepared to discuss data partitioning, artifact handling, validation design, metric selection, reproducibility and failure analysis. Reference solutions may use Python, MATLAB or another appropriate scientific computing environment. Python and MATLAB are named as appropriate options, not mandatory languages.
Evidence and screening
Relevant evidence can include an official team result, conference paper, technical report, open-source repository or meaningful code contribution. The process includes screening questions, an AI interview of approximately 30 minutes and hiring manager review. Prior work on AI training projects is not required.
Logistics
The role is global, fully remote and structured as a contractor engagement of approximately 15 hours per week. Scheduling is flexible and may include weekends. Compensation is output-based, with payment tied to tasks that meet project specifications and minimum submission requirements. Selected experts are expected to begin their first task within 24 to 48 hours after onboarding.
Pay band: $60–120/hr