Senior AI Engineer — Self-Serve ML Toolchain

UFORCE; Greater London; In loco; GBP 120.000 - 160.000; Tempo pieno

Descrizione del lavoro

UFORCE is seeking a Staff Engineer, AI / ML to build an end-to-end self-serve toolchain that lets customers adapt UFORCE models on their own private data without exposing it. You will span data processing, labeling, QA, active learning, training, evaluation, and model promotion, turning a principal-led capability into a repeatable on-site workflow.

You will design workflows for on-prem/offline environments, ensure data stays within customer boundaries, and lead a small team focused on curation,

Competenze

• 5+ years building production ML, AI, or CV systems.
• Strong Python and PyTorch proficiency.
• Experience owning ML data pipelines and evaluation infra.
• Deep CV data experience: detection, segmentation, curation and data quality.
• Hands-on model-in-the-loop or foundation-model labeling.
• Familiar with SAM-2, Grounding DINO, FiftyOne and annotation tools.
• Knowledge of held-out sets, leakage prevention, baselines and slice metrics.
• Experience with QA sampling, label-noise analysis, and missing annotations.
• Active learning or other labeling-priority methods.
• Dataset economics: quality vs. quantity, long-tail coverage.
• Dataset versioning, lineage, experiment tracking, model registries.
• Strong ownership, communication, and systems thinking.
• Experience leading engineers as a tech lead or manager.

Mansioni

• 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 pseudo-label errors, missing annotations, and long-tail data gaps.
• Develop active-learning loops that prioritize high-value frames for limited operator review.
• Build reproducible train/eval pipelines with experiment tracking, model packaging, and promotion gates.
• Design evaluation around 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.

Conoscenze

5+ years production ML/AI/CV

Python

PyTorch

ML data pipelines

Dataset curation

Active learning

QA sampling & data quality

Foundation-model labeling

Systems thinking

Technical leadership

Strumenti

SAM-2

Grounding DINO

FiftyOne

annotation platforms

Descrizione del lavoro

UFORCE is seeking a Staff Engineer, AI / ML to build an end-to-end self-serve toolchain that lets customers adapt UFORCE models on their own private data without exposing it. You will span data processing, labeling, QA, active learning, training, evaluation, and model promotion, turning a principal-led capability into a repeatable on-site workflow.

You will design workflows for on-prem/offline environments, ensure data stays within customer boundaries, and lead a small team focused on curation,