Data Intelligence Machine Learning Engineer

Role overview

Responsibilities

• check_circle Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
• check_circle Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
• check_circle Quality Assurance & Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
• check_circle Tooling & Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
• check_circle Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
• check_circle Set up and maintain robust data preparation infrastructure—optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.
• check_circle Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You’ll help transform raw data into actionable insight, supporting both research and deployment.
• check_circle Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.

About the company