AI Data Augmentation Researcher

Telvion Labs

Remote · $160k – $200k / year · Full-time · Posted 5 months ago

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About the role

Labeled data is expensive. Our clients in healthcare, manufacturing, and legal have 500 labeled examples when they need 50,000. Data augmentation can close that gap — but naive augmentation (random crops, color jitter) doesn't work for specialized domains where the augmentation must preserve domain-specific semantics. You'll research and build domain-aware augmentation strategies: generative augmentation that creates realistic training examples, consistency-preserving transformations that maintain label accuracy, and the evaluation framework that measures whether augmented data actually improves downstream model performance or just adds noise. Every augmentation strategy is a hypothesis about what variation is irrelevant to the prediction task. In medical imaging, you can flip horizontally but not vertically. In manufacturing inspection, you can vary lighting but not defect geometry. You'll need domain intuition and experimental rigor to find augmentation strategies that genuinely expand the effective training set.

Fully remote.

Responsibilities

Research domain-aware data augmentation strategies for specialized ML applications
Build generative augmentation systems creating realistic training examples
Design consistency checks ensuring augmented data preserves label accuracy
Evaluate augmentation impact on downstream model performance
Develop augmentation libraries for healthcare, manufacturing, and legal domains

Requirements

5+ years in ML research with data augmentation or few-shot learning experience
Strong PyTorch skills for generative model development
Published work in data augmentation, self-supervised learning, or domain adaptation
Statistical expertise for experiment design and significance testing
Experience working with domain experts in specialized fields