AI Active Learning Sampling Engineer

About the role

Responsibilities

• Build an active learning sampling system supporting multiple query strategies
• Implement uncertainty, diversity, and core-set sampling at 10M candidate scale
• Integrate sampling pipeline with annotation workflow APIs
• Build approximate inference for efficient candidate scoring across large unlabeled pools
• Create ROI evaluation framework comparing label efficiency vs. random sampling baseline
• Monitor sample quality and adapt strategies as models improve during training

Requirements

• 3+ years in ML engineering with experience in active learning or data-efficient training
• Strong Python and PyTorch skills
• Experience building ML inference at scale with batching and optimization
• Understanding of active learning literature and query strategy trade-offs
• Ability to design systems that integrate with existing annotation workflow platforms

Benefits

• Fully remote with flexible schedule
• Health, dental, and vision insurance
• 401(k) with match
• $3,000 professional development budget
• Annual team retreat

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