PhysioNet Competitor (Biomedical Signal Processing & ML)

About the Role

Pay: $60–$120/hour Schedule: Flexible-you choose the hours and days you work, including weekends if desired micro1 is engaging specialists with experience in the PhysioNet Challenge or comparable biomedical signal processing and machine learning competitions. In this role, you will apply your scientific programming, signal processing, and modeling expertise to create, solve, and evaluate advanced technical problems that help improve next-generation AI systems. We are looking for people who have worked directly with physiological signals and can turn noisy, real-world data into a rigorous and reproducible analysis. Strong candidates will be able to explain the methods they developed, the experiments they ran, the technical decisions they personally owned, and how they validated their results. No prior experience working on AI training projects is required. Process • Apply to the role and complete the screening questions. • Complete an AI interview of approximately 30 minutes. • Complete the hiring manager review. Compensation Structure Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Start Timeline & Availability • We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.

Responsibilities & Tasks

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

Qualifications & Requirements

• Participation in a PhysioNet/Computing in Cardiology Challenge or a comparable biomedical signal processing or scientific machine learning competition.
• Strong competition results and evidence of a substantive personal technical contribution.
• Public evidence of your work through an official team result, conference paper, technical report, open-source repository, or meaningful code contribution.
• Hands-on experience with physiological signals such as ECG, EEG, PPG, EMG, respiratory signals, polysomnography, or other clinical time-series data.