Remote job · Engineering
PhysioNet Competitor (Biomedical Signal Processing & ML)
In one line: PhysioNet Competitor (Biomedical Signal Processing & ML) is a remote role open to candidates worldwide at a company in AI training and data.
- USD 60 to 120 per hour
- Remote, worldwide
- A company in AI training and data
- Posted 19 Oct 2026
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https://keywordise.com/mcpApply to Keywordise job wcfdpr67xtAbout the role
A flexible contractor role creating rigorous, self-contained technical problems on biomedical signals, time series and machine learning, with reference solutions. Work is fully remote and global.
At a glance
- PayUSD 60 to 120 per hour
- Remote scopeRemote, worldwide
- Posted19 Oct 2026
- Last checked19 Oct 2026
What you will do
- Design self-contained problems involving physiological signals, time series, signal processing and machine learning
- Write clear reference solutions in Python, MATLAB or a similar scientific computing environment
What they look for
- Direct experience working with physiological signals and turning noisy real-world data into reproducible analysis
- Ability to explain methods developed, experiments run, technical decisions owned and how results were validated
- No prior experience with AI training projects is required
Nice to have
- Participation in a PhysioNet or Computing in Cardiology challenge, or a similar competition
- Strong competition results showing a substantive personal technical contribution
- Public evidence of work such as team results, papers, reports, open-source repositories or code contributions
- Hands-on work with ECG, EEG, PPG, EMG, respiratory, polysomnography or other clinical time-series data
Questions about this job
Is this job fully remote, and from where?
Yes, it is listed as remote, and the posting does not tie it to one country.
What does this job pay?
The posting states USD 60 to 120 per hour.
What experience is needed?
The posting asks for: direct experience working with physiological signals and turning noisy real-world data into reproducible analysis; ability to explain methods developed, experiments run, technical decisions owned and how results were validated; no prior experience with AI training projects is required.
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