Staff AI Engineer - Self-Serve ML Toolchain

UFORCE; Greater London; On-site; GBP 120,000 - 160,000; Full time

Job summary

UFORCE is a combat-systems integrator transforming battlefield experience into deployable defence technology. The Staff Engineer, AI / ML — Self-Serve Toolchain will deliver an end-to-end system enabling customers to adapt UFORCE models on their private data without exposing it.

You’ll work across data processing, labeling, QA, active learning, training, evaluation, and model promotion to create a repeatable, on-site toolchain.

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 tools such as SAM-2, Grounding DINO, FiftyOne, and annotation platforms.
• Strong understanding of evaluation: held-out sets, leakage prevention, baselines, slice metrics, and 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 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

Production ML

Python

PyTorch

ML data pipelines

Detection

Segmentation

Annotation taxonomy

Dataset curation

Data quality

Foundation-model labeling

SAM-2

Grounding DINO

FiftyOne

Annotation platforms

Active learning

Experiment tracking

Model registries

Data cards

Leadership

Tools

SAM-2

Grounding DINO

FiftyOne

Annotation platforms

lakeFS

DVC

MLflow

Weights & Biases

Kubernetes

Kubeflow

Flyte

Dagster

Airflow

Argo

Job description

UFORCE is a combat-systems integrator transforming battlefield experience into deployable defence technology. The Staff Engineer, AI / ML — Self-Serve Toolchain will deliver an end-to-end system enabling customers to adapt UFORCE models on their private data without exposing it.

You’ll work across data processing, labeling, QA, active learning, training, evaluation, and model promotion to create a repeatable, on-site toolchain.