- Dubai, Dubai, United Arab Emirates
- On-site
- Lead
- 8+ years
Design and implement in-house tools to automate data labelling pipelines, reducing reliance on manual annotation using Active Learning, Weak Supervision, and Synthetic Data Generation.
Responsibilities
- Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
- Develop 'Human-in-the-Loop' (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
- Quality Assurance & Denoising: Implement algorithmic checks to identify and correct mislabelled or 'noisy' data within existing datasets.
- Tooling & Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
- Model Optimization: Fine-tune 'teacher' models to generate high-quality pseudo-labels for 'student' models.
Requirements
- Bachelor’s degree in computer science
- Master's degree in computer science
- Engineering
- Mathematics
- Data Science