Location: Hybrid in Cambridge, UK (approximately one day per week on site; flexible for the right candidate)
auryx is on a mission to create the world's best foundation model for turning sound into health insights, transforming preventative health monitoring. We are a small, ambitious team combining cutting-edge research and world-class expertise in biomedical engineering, audio signal processing, and AI to unlock new ways of measuring human health.
Our technology uses machine learning to turn regular existing earbuds into health and fitness sensors, tracking heart rate, HRV, respiration, and advanced cardiovascular parameters, all through devices people already own. We have built early models for our core technology and are now looking for a machine learning engineer to help develop and deploy models for health and biosignal monitoring, taking our technology from proof of concept to a world-class product.
As a machine learning engineer at auryx, you will focus on developing ML models for health and biosignal monitoring, working with audio and physiological time series data. You'll transform sensor data into actionable health metrics, and contribute to developing new physiology-based features as our technology and product evolve. You will also contribute to early architecture decisions around data pipelines, model deployment, and MLOps, and spend much of your time on rapid prototyping, experimentation, and iteration based on feedback and results.
As an early employee, you will have significant potential to bring ownership and direction to our technical work while contributing across the breadth of startup activities. Beyond core ML development, you might find yourself contributing to product decisions, supporting data collection and validation efforts, or helping shape our approach to health feature development and user testing.
You will work closely with the CTO on a day to day basis, as well as our team of ML engineers, collaborating on the design, development and deployment of our ML system. The position offers the full spectrum of early-stage startup experience, from prototyping and technical problem-solving to strategic planning and cross-functional collaboration, giving you exposure to both complex technical challenges and the decision-making that defines our product roadmap and company trajectory.