- London, England, United Kingdom
- Full-time · Senior
- 5+ years
Build the end-to-end system that lets customers adapt UFORCE models on their own private data without exposing that data to us, spanning data processing, foundation-model-assisted labeling, human-in-the-loop QA, active learning, training, evaluation, and model promotion.
Responsibilities
- Own the ML adaptation pipeline from raw customer data to trained, evaluated, deployable models.
- Build foundation-model-assisted labeling workflows using tools such as SAM-2, Grounding DINO, open-vocabulary models, LLM steering, and human review.
- Design self-serve operator workflows using yes/no/maybe feedback and natural-language corrections.
- Create versioned datasets with lineage, data cards, label provenance, class distributions, and known gaps.
- Build QA methods that catch systematic pseudo-label errors, missing annotations, and long-tail data gaps.
Requirements
- Nice to have: Synthetic data
- Nice to have: Sim2real
- Nice to have: Domain randomization
- Nice to have: EO/IR
- Nice to have: LWIR