Staff AI Engineer — Self-Serve ML Toolchain (On-Prem)

UFORCE; Greater London; On-site; GBP 110,000 - 170,000; Full time

Job summary

UFORCE is a combat-systems integrator transforming battlefield experience into deployable defence technology. We seek a Staff Engineer, AI/ML — Self-Serve Toolchain to build an end-to-end system that lets customers adapt models on private data on-site, with data processing, labeling, QA, active learning, training, evaluation, and promotion.

You will design workflows with SAM-2, Grounding DINO, and open-vocabulary models, create versioned datasets, and package the toolchain for on-prem or

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 evaluation know-how: held-out sets, leakage controls, baselines, slice metrics, promotion gates.
• Experience with QA sampling, label-noise analysis, missing annotations, IAA, or alternatives when only one operator is available.
• Experience with active learning or other methods for prioritizing labeling effort.
• Ability to reason about dataset economics: quality vs. quantity, long-tail coverage, and cost-per-useful-example.
• Experience with dataset versioning, lineage, experiment tracking, model registries, or data cards.
• Strong ownership, communication, and systems thinking.
• 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 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 pipelines

Dataset versioning

Active learning

LLM tooling

On-prem deployment

Team leadership

Tools

SAM-2

Grounding DINO

FiftyOne

annotation platforms

lakeFS

DVC

MLflow

Weights & Biases

Kubernetes

Kubeflow

Job description

UFORCE is a combat-systems integrator transforming battlefield experience into deployable defence technology. We seek a Staff Engineer, AI/ML — Self-Serve Toolchain to build an end-to-end system that lets customers adapt models on private data on-site, with data processing, labeling, QA, active learning, training, evaluation, and promotion.

You will design workflows with SAM-2, Grounding DINO, and open-vocabulary models, create versioned datasets, and package the toolchain for on-prem or