Wearable Health ML Scientist In-Ear Signals

OmniBuds; Cambridge; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

OmniBuds is seeking an Applied ML Scientist in Cambridge to turn multimodal, noisy wearable signals into reliable physiological insights. You will work at the intersection of ML, signal processing, and physiological sensing for ear-worn devices.

You will lead projects from signal characterisation to prospective evaluation, collaborating with clinical science, sensing, hardware, and firmware teams to deploy efficient algorithms for real-world use.

Qualifications

• Strong background in applied machine learning and signal processing.
• Experience with multimodal, physiological or biomedical sensor data.
• Proficiency in Python and modern ML frameworks.
• Good understanding of model validation, uncertainty, bias and robustness.
• Experience with wearable/edge ML and resource-constrained inference is a plus.
• Ability to work across ML, clinical science, sensing, hardware and engineering.

Responsibilities

• Develop signal-processing and machine-learning methods for multimodal in-ear physiological signals, including PPG, cardiovascular acoustics, IMU, and temperature.
• Build models that fuse complementary sensing modalities to infer cardiovascular and autonomic physiology from noisy, incomplete data.
• Develop methods for signal-quality assessment, measurement opportunity detection, uncertainty estimation, calibration, and model abstention.
• Lead the scientific pipeline from signal characterisation and representation learning through model development, validation, and prospective evaluation.
• Investigate multimodal fusion, temporal modelling, representation learning, and hybrid physiological + ML approaches.
• Design rigorous experiments and ablation studies to understand information use and reliability of inferences.
• Collaborate with clinical data scientists, clinicians, hardware, and firmware teams to optimize the sensing and inference system.
• Translate research models into efficient algorithms suitable for real-world and on-device deployment.
• Contribute to OmniBuds’ roadmap for blood-pressure estimation and longitudinal cardiovascular health.

Skills

Applied ML

Signal processing

Time-series modelling

Computational physiology

Python

Model validation

Uncertainty estimation

Robustness

Cross-disciplinary collaboration

Tools

Python (ML frameworks)

Job description

OmniBuds is seeking an Applied ML Scientist in Cambridge to turn multimodal, noisy wearable signals into reliable physiological insights. You will work at the intersection of ML, signal processing, and physiological sensing for ear-worn devices.

You will lead projects from signal characterisation to prospective evaluation, collaborating with clinical science, sensing, hardware, and firmware teams to deploy efficient algorithms for real-world use.