Machine Learning Engineer

About the role

DigeHealth is developing wearable technology to monitor gastrointestinal health. As a Machine Learning Engineer you will design and implement AI solutions that transform raw bowel sound data into actionable clinical insights, working closely with physicians, researchers and a fast‑moving startup team.

Key responsibilities

• Refine and improve bowel sound recognition algorithms to achieve high accuracy across diverse environments.
• Develop predictive machine‑learning models for detecting bowel health events and disorders.
• Apply audio signal‑processing techniques such as noise reduction and sound classification for gastrointestinal applications.
• Own end‑to‑end tasks, navigate ambiguity and contribute to the overall product roadmap in a rapid‑pace startup.
• Manage large datasets, ensuring data quality, usability and compliance for model training and evaluation.

Required profile

• Bachelor’s or Master’s degree in Computer Science or a related field.
• Hands‑on experience building machine‑learning algorithms for computer vision, sound analysis or audio signal processing.
• Experience with large datasets and production‑grade ML models.
• Familiarity with healthcare or biomedical applications is a plus.
• Fluent English; other languages not required.

Required skills

• Python programming.
• TensorFlow or PyTorch frameworks.
• Audio signal‑processing techniques.
• Large‑scale data handling and model deployment.
• Edge processing and embedded‑system deployment of ML models (preferred).
• Cloud data engineering on Azure, including CI/CD pipelines (preferred).
• Statistical data analysis for predictive modelling (preferred).