Machine Learning Engineer

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.