Data Annotation Lead: Scale Quality & Team

Kindredventures; United States; On-site; USD 90,000 - 140,000; Full time

Job summary

Mecka AI in NYC / Toronto—onsite—seeks a Data Annotation Lead to own its annotation operation end-to-end and to build the in-house team with a strong PM lean. You’ll define guidelines, QA, and tooling, while scaling a workforce and partnering with ML/CV, product, and operations to enable fast experiments.

You’ll translate research requirements into actionable guidelines, stand up a quality system, and drive AI-assisted labeling to increase throughput and reduce costs.

Qualifications

• Direct experience in data annotation / labeling is required.
• Background in data collection, robotics, or text annotation is a plus.
• Proven ability to build or scale a team or function.
• PM instincts with prioritization under ambiguity.
• Strong written and verbal guidelines are the source of truth.
• Familiar with annotation tooling and dashboards.
• Understanding of ML impact on annotation quality.

Responsibilities

• Own annotation operations end-to-end — quality, throughput, and cost per delivered hour.
• Build and lead the in-house annotation team; manage in-house vs. partner/BPO mix.
• Translate research and client requirements into clear annotation guidelines, taxonomies, and QA rubrics.
• Stand up the quality system: audits, inter-annotator agreement, golden sets, reviewer scorecards.
• Partner with ML/CV and product to spec and pilot new annotation task types.
• Drive AI-assisted labeling to raise throughput and cut cost.
• Own the metrics — dashboards on quality and volume — and report to leadership.
• Annotate yourself when a new task type is being designed.

Skills

Direct data annotation experience

Team leadership

PM & operations

Stakeholder management

Strong communication

AI tooling familiarity

ML understanding

Job description

Mecka AI in NYC / Toronto—onsite—seeks a Data Annotation Lead to own its annotation operation end-to-end and to build the in-house team with a strong PM lean. You’ll define guidelines, QA, and tooling, while scaling a workforce and partnering with ML/CV, product, and operations to enable fast experiments.

You’ll translate research requirements into actionable guidelines, stand up a quality system, and drive AI-assisted labeling to increase throughput and reduce costs.