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Hire a Biomedical Signal Processing Engineer
A biomedical signal processing engineer turns raw physiologic sensor data (PPG, accelerometry, respiratory airflow) into measurements and models that hold up against a clinical reference. Hire for both DSP and validation experience, not ML alone. Talent Badger's bench includes a PhD biomedical engineer with 13+ years of post-doctoral work in medical-device signal processing and ML for sleep and neurology, available fractionally.
What this specialist works on
- PPG processing: heart rate and pulse-derived features, signal-quality indices, and motion-artifact rejection using the accelerometer as a reference channel.
- Respiratory and sleep signals: airflow, effort and movement data turned into events and stages that can be compared against a scored reference.
- Feature engineering and ML: classical DSP features first, deep models where the data supports them, and an honest comparison between the two.
- End-to-end pipelines: capture firmware and data formats, labeling, training, and deployment on the device, the phone or the cloud.
The parts teams underestimate
- Validation design: write down the metric, the reference device and the acceptance threshold before the first model is trained.
- Subject-level splits: train and test on different people. Putting windows from the same subject in both sets inflates accuracy and hides the problem until the field.
- Population coverage: skin tone, motion, sensor fit and placement all change a PPG signal, so the collection plan has to span them.
- Deployment limits: on-device algorithms live within fixed memory, clock and battery budgets, which often means fixed-point code and simpler models.
- Intended use: the claims you make decide whether the algorithm is part of a regulated medical device, and that changes how it must be documented.
How to scope a first engagement
- Start with a data audit: what you have, how it was labeled, and what reference it was collected against.
- Prototype offline, typically in Python, against a fixed dataset before touching firmware.
- When the signal itself is the problem, pair with the hardware side: an optics specialist for the PPG light path, an EE for the analog front end. Both are on the same bench.
- Milestone the work: baseline algorithm, validated performance on held-out subjects, then the on-device port.
Common questions
A data scientist can train a model on whatever data you hand them. A biomedical signal processing engineer understands where the signal comes from: sensor physics, physiologic noise, motion artifact and the reference methods clinicians trust. That knowledge keeps a model from learning the artifact instead of the physiology.