Experience with weak supervision and active learning
The role focuses on designing end-to-end automated labelling systems to reduce reliance on manual annotation through techniques like Active Learning and Synthetic Data Generation
Job Summary
The role focuses on designing end-to-end automated labelling systems to reduce reliance on manual annotation through techniques like Active Learning and Synthetic Data Generation.
You will bridge the gap between raw data collection and model-ready datasets by implementing algorithmic checks to identify and correct noisy data.
This position requires collaborating with software engineers and product teams to integrate labelling tools with existing data lakes and MLOps infrastructure.
Matching Summary
The role focuses on designing end-to-end automated labelling systems to reduce reliance on manual annotation through techniques like Active Learning and Synthetic Data Generation.
Skills & Requirements
Must-have
8+ years Machine Learning engineering experience
Proficiency in Python and PyTorch or TensorFlow
Experience with Weak Supervision and Active Learning
Expertise in building Human-in-the-Loop systems
Knowledge of SQL, NoSQL, and large-scale data
Nice-to-have
Experience with Snorkel or Cleanlab frameworks
Familiarity with AWS SageMaker Ground Truth
Strong background in feature engineering and visualization
Ability to collaborate across global engineering teams
Experience with DVC for data version control
Key Requirements
Bachelor's or Master's degree in Computer Science or related field
At least 8+ years of professional ML engineering experience
Proven track record in data-centric AI or computer vision/NLP