PhysioNet Competitor (Biomedical Signal Processing & ML)
$60 - $120 per hourmicro1
Pay: $60 - $120/hour
Required Skills
Biotechnology
Biology
Machine Learning
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.
Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.
Role Title: PhysioNet Competitor (Biomedical Signal Processing & ML)
Pay: $60–$120/hour
Location: Global, fully remote
Job Type: Contractor (~15 hours per week)
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.
Scope of Work
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.
Preferred Qualifications
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.
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.
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