A health technology company is seeking an On-Device ML Engineer to develop machine learning models that run directly on wearable devices, extracting reliable health signals under strict real-world constraints.
This is a technically deep and highly impactful role, sitting at the intersection of signal processing, applied ML, and embedded systems. You’ll work closely with hardware and firmware teams to optimise end‑to‑end sensing pipelines, tackling problems that very few teams in the world are working on. You’ll have significant ownership over algorithm development from signal cleaning through to prototype integration.
In this position, you’ll develop physiological inference algorithms for wearable health products, build methods to extract reliable cardiovascular and autonomic health metrics from real-world data, and advance hybrid DSP + ML approaches for continuous health sensing — all within tight compute and power budgets.
What they’re looking for:
- Strong background in signal processing and applied machine learning
- Experience deploying ML models on embedded or edge devices
- Proficiency in Python; C/C++ experience is a plus
- Understanding of physiological signals and noisy, real-world sensor data
- Ability to balance accuracy, efficiency, and robustness under hardware constraints
Why consider it:
- Work on frontier problems in medical‑grade wearable inference
- High ownership across the full algorithm pipeline, from research to integration
- Close collaboration across ML, hardware, and firmware disciplines
- Early‑stage company with significant growth potential and technical influence
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