Staff AI Engineer: Self-Serve ML Toolchain

United States Digital Space LLC; Greater London; Remote; GBP 114,000 - 159,000; Full time

Job summary

United States Digital Space LLC is seeking a Staff Engineer for AI/ML to own the end-to-end self-serve adaptation toolchain. You will enable customers to adapt models on private data without exposing it externally, covering data processing, labeling, QA, active learning, training, evaluation, and model promotion, with on-site deployment options.

You will build reproducible pipelines, versioned datasets, and evaluation methods while leading a small team focusing on curation, auto-labeling,

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

Python

PyTorch

Computer vision

ML data pipelines

Model-in-the-loop

Active learning

Grounding DINO

SAM-2

FiftyOne

Annotation tools

Leadership

Systems thinking

Tools

SAM-2

Grounding DINO

FiftyOne

Annotation platforms

Job description

United States Digital Space LLC is seeking a Staff Engineer for AI/ML to own the end-to-end self-serve adaptation toolchain. You will enable customers to adapt models on private data without exposing it externally, covering data processing, labeling, QA, active learning, training, evaluation, and model promotion, with on-site deployment options.

You will build reproducible pipelines, versioned datasets, and evaluation methods while leading a small team focusing on curation, auto-labeling,