Machine Learning Engineer - Harnham

Cambridge (1 day a week)

Up to £80,000 + Equity + Benefits

We’re working with a pioneering health-tech start-up that’s transforming the way we measure human health through sound. Their mission is to create the world’s leading foundation model for turning sound into health insights — enabling preventative health monitoring through devices people already own.

They’re now looking for a Machine Learning Engineer to build and optimise on‑device ML models for health and biosignal monitoring, helping take their technology from proof of concept to a production‑ready product.

You’ll be at the forefront of developing models that run efficiently on constrained devices, working closely with the CTO on design, optimisation, and deployment. This is a hands‑on technical role that offers full exposure to the early‑stage startup experience — from prototyping and experimentation to strategic product decisions.

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• Develop, optimise, and deploy machine learning models for on‑device health monitoring.
  
• Experiment with architectures and apply techniques such as quantisation, pruning, and compression to improve efficiency.
  
• Collaborate with cross‑functional teams to take research prototypes into production‑ready systems.
  
• Contribute to broader technical and product discussions, including data collection, validation, and feature development.
  
• Take ownership of projects, working autonomously while supporting the wider engineering team.
  

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• Ph.D. or Master’s degree in Computer Science, Machine Learning, Information or Biomedical Engineering (or similar).
  
• Strong experience with deep learning frameworks (PyTorch/TensorFlow) and Python development.
  
• Proven background in on‑device ML (TinyML) using frameworks such as TensorFlow Lite, ExecuTorch, or TVM.
  
• Solid understanding of model optimisation for constrained hardware environments.
  
• Ability to write clean, maintainable, and well‑tested code in a collaborative setting.
  
• Curiosity, adaptability, and enthusiasm for working in a fast‑paced, early‑stage environment.
  
• Experience working with time‑series data such as audio or biosignals.
  
• Background in biomedical or signal processing.
  
• Experience writing production‑level code or integrating models with embedded systems.
  
• Previous startup experience or exposure to medical device development.