About the Role :

We're looking for a hands-on ML engineer to build and ship models across Hoomanely's sensor-driven product ecosystem, working closely with the ML Lead on digital signal processing, computer vision, and time series problems. You're comfortable taking a model from an early prototype through to a reliable, production-deployed system.

Responsibilities :

- Build and iterate on ML models for time series, audio signal, and sensor fusion problems, under the guidance of the ML Lead.

- Build and maintain data preprocessing and feature engineering pipelines that prepare sensor data for training and inference at scale.

- Package and deploy models into production, including on edge or embedded compute, with attention to latency and resource constraints.

- Run structured experiments to evaluate model accuracy, generalisation, and failure modes, and document findings clearly.

- Support monitoring and retraining pipelines that track model performance and drift after deployment.

- Work closely with firmware, hardware, and app teams to turn model outputs into clear, actionable product insight.

Requirements :

- 3+ years of applied machine learning experience.

- Solid understanding of time series analysis and at least one of CNNs, RNNs, or signal processing techniques.

- Proficient in Python and at least one ML framework (TensorFlow or PyTorch).

- Taken at least one model from prototype to a production or near-production deployment.

Bonus Skills :

- Experience with audio signal processing or accelerometer-based activity classification.

- Familiarity with edge deployment of ML models on embedded compute.

- Exposure to MLOps tools and practices.

- Prior experience in IoT, wearables, or health-tech.