Applied Machine Learning Scientist

Cambridge, UK

Research, Algorithms & Engineering

Full-time | Hybrid

As an Applied ML Scientist, you will develop machine-learning methods that turn multimodal, noisy wearable signals into reliable physiological and clinical insights
. You will work at the intersection of machine learning, signal processing, and physiological sensing, developing models that can recover cardiovascular and autonomic information from real-world data and ultimately operate within the constraints of ear-worn devices.

• 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, and context-dependent 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 what information models are using, when inference is reliable, and where it fails.
• Work closely with clinical data scientists, clinicians, hardware, and firmware teams to optimise the complete sensing and inference system.
• Translate research models into efficient algorithms suitable for real-world and ultimately on-device deployment.
• Contribute to Omni Buds’ scientific roadmap for blood-pressure estimation, hypertension, and longitudinal cardiovascular health.

• Strong background in applied machine learning, signal processing, time-series modelling, or computational physiology.
• Experience developing ML methods for multimodal, physiological, biomedical, or other complex sensor data.
• Strong proficiency in Python and modern ML frameworks.
• Good understanding of model validation, generalisation, uncertainty, bias, and robustness.
• Experience with wearable/edge ML or resource-constrained inference is valuable, but not essential.
• Ability to go beyond maximising model accuracy and ask the harder scientific questions: why does the model work, when does it work, and when should we not trust it?
• Ability to collaborate across ML, clinical science, sensing, hardware, and engineering.

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.