Senior Data Annotator

About this Role:

Own annotation quality for a delivery team. A Senior Annotator personally handles the hardest annotation work, runs the QA process everything else passes through, trains and develops junior annotators, and serves as the final check before a dataset goes to the customer.

Key Responsibilities:

Quality Assurance:

• Execute the review and audit plan for each batch, including which work gets sampled at what rate and when a batch gets sent back.
• Perform frame-level and sequence-level review against the ontology and acceptance criteria; adjudicate disputed labels and issue a binding call.
• Monitor platform quality reporting and annotator performance data, and act on what it surfaces.
• Maintain gold-standard and benchmark task sets used to check individual and team accuracy.
• Run consensus reviews on overlapping assignments and drive the guideline changes that close recurring disagreement.
• Manage the rework loop: route corrections, verify closure, and confirm the fix generalizes across the batch, not just the sampled frames.

Training Junior Annotators:

• Onboard new annotators onto the ontology, tooling, and workflow, and certify them against a benchmark set before releasing them onto production work.
• Build and maintain training materials: worked examples, edge-case libraries, and short reference guides for each project.
• Run calibration sessions where the team works the same difficult frames and reconciles differences against a documented standard.
• Coach individuals using specific examples from their own reviewed work, and confirm the correction holds on the next batch.
• Identify who is ready for harder task types or a new modality, and who needs additional support.
• Serve as the first escalation point for annotator questions, answering in a way that turns a one-off answer into a documented rule.
• Help screen candidates through practical annotation assessments.

Delivery and guidelines:

• Serve as the quality gate for customer deliveries and provide the quality summary that accompanies each one.
• Author and version labeling guidelines, decision trees, and edge-case libraries derived from the customer ontology.
• Consolidate annotator questions into ontology clarification requests and route them through program management.
• Perform the hardest annotation work personally: dense scenes, long tracking sequences, degraded or ambiguous imagery.
• Surface quality, staffing, and capacity risk to program management early enough to act on.

Must-Have Qualifications: 

• 3+ years of image and video annotation experience, including 1+ year in a QA, review, lead, or adjudication capacity.
• Demonstrated ability to define quality criteria and review against them — not only to label well personally.
• Experience training or mentoring annotators, with evidence of measurable improvement in their output.
• Solid grasp of what annotation quality metrics indicate and how to act on them.
• Experience writing labeling guidelines or SOPs that other people worked from.
• Ability to communicate quality risk clearly and early to program management.

Nice-to-Have Qualifications:

• Experience across multiple domains — aerial, overhead, automotive, industrial, medical, or defense imagery.
• Experience with synthetic data and the failure modes that distinguish it from real sensor data.
• Experience with model-assisted labeling pipelines.
• Experience supporting milestone-based deliveries with defined acceptance criteria.
• Prior government or regulated-industry program experience.

Program eligibility:  Some programs require eligibility for a government background investigation or credentialing. Assignment to those programs is contingent on meeting those requirements.