Machine Learning Engineer - Harnham
Jobster
- Cambridge, England, United Kingdom
- Cambridge, England, United Kingdom
Über
Up to £80,000 + Equity + Benefits
About The Role 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.
Key Responsibilities
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.
What We’re Looking For
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.
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Sprachkenntnisse
- English
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