Machine Learning Engineer

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:

• You have 3+ years of applied machine learning experience.
• You have a solid understanding of time series analysis and at least one of CNNs, RNNs, or signal processing techniques.
• You're proficient in Python and at least one ML framework (TensorFlow or PyTorch).
• You've taken at least one model from prototype to a production or near-production deployment.

Bonus:

• 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.