Location

Cambridge, UK | Nashville, USA

Department

Research, Algorithms & Engineering

Employment Type

Full-time | Hybrid

About The Role

As an On-Device ML Engineer, you will develop machine-learning models that run directly on ear-worn devices. Your work will focus on extracting reliable cardiovascular and autonomic health signals from noisy, real-world data under strict compute and power constraints.

What you’ll do

Develop signal-processing and physiological-inference algorithms for multimodal in-ear bio-signals (PPG, acoustics, IMU, temperature).
Build methods that convert noisy, real-world data into reliable cardiovascular and autonomic health metrics.
Lead algorithm pipelines from signal cleaning to model design, validation, and prototype integration.
Work closely with hardware and firmware teams to optimise end-to-end sensing systems.
Advance hybrid DSP + ML approaches for ear-based health sensing.
Contribute to OmniBuds’ roadmap for continuous BP estimation and hypertension-focused digital biomarkers.

What we expect

Strong background in signal processing and applied machine learning.
Experience deploying ML models on embedded or edge devices.
Proficiency in Python; experience with C/C++ is a plus.
Understanding of physiological signals and noisy sensor data.
Ability to balance accuracy, efficiency, and robustness.

Why OmniBuds

You’ll work on problems few teams in the world are tackling—bringing continuous, medical-grade inference onto tiny devices worn all day, every day.

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