PhysioNet Competitor (Biomedical Signal Processing and ML)

As a PhysioNet Competitor, you will create self-contained problems based on real physiological signals and develop clear reference solutions. Your work will support the training and evaluation of AI systems that handle biomedical data.

• Create challenging problems involving biomedical signals and clinical time-series data.
• Develop reference solutions in Python, MATLAB, or another scientific computing language.
• Design tasks involving preprocessing, filtering, segmentation, feature extraction, representation learning, classification, or forecasting.
• Validate your methods and make sure your work is reproducible.
• Submit completed work that meets the project specifications.

This is a part-time contract role for someone with hands-on experience in biomedical signal processing, scientific machine learning, and competition-based technical work.

• Pay: $60 to $120 per hour equivalent, paid per task that meets the project specifications.
• Time: Less than 20 hours per week.
• Location: Open to candidates across the eligible countries in North and South America, Europe, Asia, Africa, and the Middle East.
• Language: English fluency.
• Experience: Participation in a PhysioNet/CinC Challenge or a comparable biomedical signal processing or scientific machine learning competition.
• Technical evidence: Public evidence of substantial personal work, such as a team result, paper, technical report, repository, or meaningful code contribution.
• Signal experience: ECG, EEG, PPG, EMG, respiratory signals, polysomnography, or other clinical time-series data.
• Tools: Python, MATLAB, or another scientific computing language.
• Prior AI training experience is not required.

You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.

AI training is the human work behind systems that learn from examples, including writing problems, preparing data, and reviewing model results. People with strong technical or subject knowledge are paid to create accurate examples and evaluate whether AI-generated work is correct. OpenTrain is a free platform where contributors find these projects, build a profile, and apply.