Staff AI Engineer — Self-Serve ML Toolchain

Uforce; Greater London; Remote; GBP 120,000 - 190,000; Full time

Job summary

UFORCE, a London-headquartered defence-technology company, seeks a Staff Engineer to build an end-to-end AI/ML self-serve toolchain that enables customers to adapt models on their own private data on-site.

You will own the adaptation pipeline, design foundation-model-assisted labeling, active learning, and reproducible train/eval pipelines, while leading a small specialist team around curation and deployment.

Qualifications

• 5+ years building production ML, AI, or computer-vision systems.
• Strong Python and PyTorch.
• Experience owning ML data pipelines, training pipelines, or evaluation infrastructure.
• Deep CV data experience: detection, segmentation, annotation taxonomies, dataset curation, and data quality.
• Hands-on experience with model-in-the-loop or foundation-model-assisted labeling.
• Familiarity with SAM-2, Grounding DINO, FiftyOne, and annotation platforms.
• Strong understanding of evaluation: held-out sets, leakage prevention, baselines, slice metrics, and promotion gates.
• Experience leading engineers as a tech lead, staff engineer, or small-team manager.

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.
• Develop active-learning loops that prioritize the highest-value frames for limited operator review.
• Build reproducible train/eval pipelines with experiment tracking, model packaging, and promotion gates.
• Design evaluation around held-out anchor sets, leakage prevention, baselines, slice metrics, and automated approve/reject decisions.
• Package the toolchain for on-prem, air-gapped, regulated, or customer-held environments.
• Close the field-failure loop by feeding live failures back into the next tune cycle.
• Lead and grow a small specialist team around curation, auto-labeling, deployment, and onboarding.

Skills

Python

PyTorch

ML data pipelines

Foundation models

LLM tooling

Dataset versioning

Leadership

Tools

SAM-2

Grounding DINO

FiftyOne

Annotation platforms

Job description

UFORCE, a London-headquartered defence-technology company, seeks a Staff Engineer to build an end-to-end AI/ML self-serve toolchain that enables customers to adapt models on their own private data on-site.

You will own the adaptation pipeline, design foundation-model-assisted labeling, active learning, and reproducible train/eval pipelines, while leading a small specialist team around curation and deployment.