The RoleWe're hiring a
Lead, Data Quality to own the quality systems, QA team, and customer-ready standard for Mecka's robotics training data. This is a data-operations leadership role, not a software QA role: you will define what good data means, measure it, improve it, and build the review systems that keep quality high as volume scales.
You'll manage QA reviewers and quality analysts, partner closely with data operations, product, engineering, and customer teams, and be accountable for whether datasets are accurate, complete, consistent, and useful to frontier AI labs.
What You'll OwnData Quality Standards- Define acceptance criteria for robotics datasets across labels, trajectories, video, sensor streams, metadata, task outcomes, edge cases, and customer-specific requirements.
- Build rubrics, severity levels, rejection taxonomies, sampling rules, and customer acceptance gates.
- Translate ambiguous customer requirements into measurable internal QA specs that operators and reviewers can execute.
QA Systems and Review Operations- Design the QA layers for each project: first-pass checks, reviewer queues, audit sampling, escalation paths, adjudication, and final release approval.
- Build golden datasets, calibration tasks, inter-reviewer agreement checks, and reviewer drift monitoring.
- Balance review coverage, speed, cost, and customer risk; know when to inspect, when to sample, and when to automate.
Team Leadership- Manage, coach, and scale a team of QA reviewers, quality analysts, and review leads.
- Set review SLAs, workload allocation, training standards, performance expectations, and escalation norms.
- Build a quality culture where data operators own first-pass quality and QA prevents repeat defects instead of only catching them after the fact.
Quality Analytics and Continuous Improvement- Own dashboards for audit pass rate, defect rate, customer rejection rate, reviewer throughput, rework, cost per approved item, and root-cause trends.
- Run postmortems on quality misses and drive corrective actions across guidelines, training, tooling, and process design.
- Partner with engineering to build automated validation checks for schema completeness, duplicates, time sync, metadata coverage, outliers, and model-assisted review.
Who You AreRequired Background- 5+ years in data quality, data operations, annotation QA, trust and safety quality, autonomy data ops, manufacturing quality, or comparable high-volume review operations.
- 2+ years managing reviewers, analysts, auditors, or operations leads.
- Strong analytical ability with spreadsheets, dashboards, SQL, BI tools, or Python; you can diagnose quality problems with data, not anecdotes.
- Experience building rubrics, SOPs, sampling plans, QA workflows, calibration programs, or customer acceptance criteria.
Strong Signals- Built a QA system from scratch or materially improved one that was already running.
- Owned customer-facing quality metrics, escalations, or dataset acceptance.
- Familiarity with robotics, computer vision, video, sensor data, annotation platforms, or multimodal datasets.
You Are- Precise about definitions and practical about execution.
- Comfortable pushing for high standards without slowing the business unnecessarily.
- Calm in escalations and rigorous in root-cause analysis.
- A manager who can coach reviewers with empathy while holding a high bar.
Why This Role- Own the quality bar for datasets used by the foundation model teams shaping the next decade of robotics.
- Build the QA function while Mecka scales from startup execution to repeatable data infrastructure.
- Work across robotics, data operations, product, engineering, and customer delivery instead of sitting in a narrow QA lane.
- Turn messy real-world physical data into trusted training data customers can build on.
- Join a Series B company where quality is a core advantage, not a back-office process.
What Success Looks Like- Mecka has a clear, documented quality standard for every major data product and customer project.
- QA review throughput scales without sacrificing customer acceptance rates or dataset usefulness.
- Defect taxonomies, dashboards, and recurring quality reviews are used by operations and product leadership every week.
- Reviewer calibration is consistent, measurable, and repeatable across projects and modalities.
- Quality misses trigger fast root-cause action, not blame, and repeat defects decline materially over 12 months.
