Data Annotation Lead

Data Annotation Lead Team: Data Operations · Type: Full-time · Location: NYC / Toronto — on-site About Mecka Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI and robotics teams. Our operations span multiple countries, with contributors, partners, and customers across North America and Asia.

Robotics will become the largest industry in human history — larger than anything that has come before it. As intelligent machines move into the physical world, they will dramatically expand global GDP, raise the material standard of living for everyone, and ultimately help make humanity a multiplanetary civilization . None of that happens without one thing: enormous amounts of high-quality, real-world data. Mecka AI builds that foundation. We are the data infrastructure layer for robotics and embodied AI — the substrate that teaches machines to perceive, reason, and act in reality. Get this right, and we accelerate the most important technological transition of our time.

We're looking for a Data Annotation Lead to own our annotation operation end-to-end and build the team behind it. This is a lead / player-coach role with a heavy PM lean: you'll set the framework, quality bar, and tooling for annotation, turn ambiguous research requests into crisp guidelines, and scale a workforce that can pivot fast without dropping quality. You'll sit at the seam of ML/CV, product, and operations.

Own annotation operations end-to-end — quality, throughput, and cost per delivered hour Build and lead the in-house annotation team; own the in-house vs. partner/BPO mix and manage external vendors where used 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, escalation paths Partner with ML/CV and product to spec and pilot new annotation task types for fast-moving experiments Drive AI-assisted labeling (model pre-labels → human correction) to raise throughput and cut cost Own the metrics — dashboards on quality and volume — and report the state of annotation to leadership Stay in the weeds: annotate yourself whenever a new task type is being designed What you'll bring Direct experience in data annotation / labeling (required — the thing we care most about) Ideal background in data collection, physical AI / robotics, or text annotation Proven people and operations leadership — you've built or scaled a team or function PM instincts: ruthless prioritization of competing requests, organized execution under ambiguity, strong stakeholder management across annotators, engineering, and clients Clear written and verbal communication — your guidelines are the source of truth for the team Comfort with annotation tooling and the ability to leverage AI coding tools to build internal trackers/dashboards (no formal CS background required) Intermediate understanding of ML and how annotation quality drives model performance A level of hardcore-ness while still treating people like people Nice to have Previously led a data annotation team, or were a top-tier annotator yourself Vendor / BPO management experience, ideally where quality was the primary lever Familiarity with text annotation styles and video concepts (frame rate, keypoints, bounding boxes, temporal segments) Experience managing distributed or offshore teams