Senior AI Engineer

UFORCE

. Build the end-to-end self-serve ML adaptation toolchain for customers to adapt UFORCE models on private data without exposing that data to UFORCE .

Posted 11/12/2026full-timeLondon • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building production ML and AI systems, with a strong focus on Python and PyTorch. Capable of managing ML data pipelines, evaluation infrastructure, and leading teams in developing self-serve ML adaptation tools.

Highest-signal resume keywords

Production ML SystemsPython ProgrammingPyTorch FrameworkML Data PipelinesComputer Vision Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningComputer VisionDataset VersioningExperiment TrackingActive LearningLabel Noise AnalysisModel PackagingEvaluation InfrastructureData Quality AssessmentAnnotation Taxonomies

Soft Skills

Strong OwnershipEffective CommunicationSystems ThinkingTeam Leadership

Tools & Technologies

SAM-2Grounding DINOFiftyOneMLflowWeights & BiasesKubernetesKubeflowFlyteDagsterAirflow

Industry Keywords

Privacy-Preserving MLFederated LearningSynthetic DataRemote SensingTactical SystemsLong-Tail CoverageOn-Prem DeploymentAir-Gapped EnvironmentsModel RegistriesC2/C4ISR

Tech Stack

Tools & technologies

AirflowKubernetesPythonPyTorchRemote Sensing

About the role

Key responsibilities & impact

Build the end-to-end self-serve ML adaptation toolchain for customers to adapt UFORCE models on private data without exposing that data to UFORCE
Own the ML adaptation pipeline from raw customer data through training, evaluation, and 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 to detect pseudo-label errors, missing annotations, and long-tail data gaps
Develop active-learning loops to prioritize high-value frames for operator review
Build reproducible training and evaluation pipelines with experiment tracking, model packaging, and promotion gates
Design evaluation using held-out anchor sets, leakage prevention, baselines, slice metrics, and automated approval/rejection decisions
Package the toolchain for on-premises, air-gapped, regulated, or customer-held environments
Feed live field failures into subsequent tuning cycles
Lead and grow a small specialist team covering curation, auto-labeling, deployment, and onboarding
Enable customers to complete adaptation cycles without engineer intervention and keep customer data within their boundaries
Establish trusted datasets, measured pseudo-label quality, evidence-based model promotion, and reproducible field-failure inputs

Requirements

What you’ll need

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 computer-vision 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 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, including quality versus 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
Nice to have: synthetic data, sim2real, or domain randomization experience
Nice to have: EO/IR, LWIR, remote sensing, maritime imagery, or small-object detection experience
Nice to have: privacy-preserving ML, federated learning, on-prem, or air-gapped deployment experience
Nice to have: self-serve ML platforms or tools for non-expert users
Nice to have: lakeFS, DVC, MLflow, Weights & Biases, Kubernetes, Kubeflow, Flyte, Dagster, Airflow, or Argo
Nice to have: LLM application, context engineering, structured output, or LLM evaluation experience
Nice to have: radar, AIS, EO/IR fusion, tracking, sensor fusion, robotics, autonomy, UxV, defence tech, C2/C4ISR, or tactical systems exposure
Ability to work legally in the country for which applying; sponsorship requirements are requested in the application form

Benefits

Comp & perks

Hybrid workplace
Opportunity to work on defence technology, autonomy software, robotics, and uncrewed platforms
Opportunity to lead and grow a specialist team
Work involving on-prem, air-gapped, regulated, or customer-held environments