Human Data Architect, Quality

About the Role

We're hiring a Human Data Architect, Quality to be the person with taste for what robotics training data should look like at Mecka. You will define what good data is — the labeling rubrics, ontologies, schemas, sampling philosophy, and acceptance criteria that every dataset we ship is measured against. You decide what goes in or out of a dataset and why. This is a standards-and-methodology architecture role, not a QA-management role. You set the quality bar; data operations and QA teams enforce it. Your output is the spec the entire data org and our customers run on.

You will work shoulder-to-shoulder with foundation-model researchers at our customers to translate model behavior into data structure — what to label, how to label it, how to organize it, how to compose a training set, what the edge cases are, and what makes a dataset trainable versus merely large.

What You'll Own

Labeling Rubrics & Quality Criteria (per customer)

• Define the labeling rubrics, severity levels, rejection taxonomies, and acceptance criteria for each customer program across video, sensor streams, trajectories, action labels, task outcomes, language grounding, and metadata.
• Translate ambiguous customer requirements ("we want a model that can do X") into precise, measurable, executable data specifications.
• Maintain customer-specific quality criteria and the canonical data dictionary every program references.
• Build golden datasets, reference examples, and calibration tasks that define "correct" by demonstration, not just description.

Ontology & Data Organization

• Own the taxonomy, schema, and class hierarchies for robotics datasets — how attributes are structured, how temporal segmentation works, how event boundaries are defined, how ambiguity is handled, how edge cases are categorized.
• Decide how data is organized end-to-end so it is trainable, queryable, and composable across customers and modalities.
• Set dataset versioning conventions, schema evolution rules, and the data-organization philosophy the org runs on.

Dataset Composition — What's In, What's Out

• Own the philosophy for what goes into a dataset and what gets cut: distribution, diversity, edge-case representation, redundancy, license/provenance constraints.
• Decide sampling strategies, balancing rules, and curation principles for each program.
• Make taste-driven calls on what data is worth collecting at all — and push back when collection plans won't produce trainable data.
• Define the acceptance bar that says "this dataset is ready to ship" — and hold it under deadline pressure.

Methodology Iteration from Model Signal

• Iterate rubrics and ontology based on model-failure signal from customers — your standards evolve with what models actually struggle to learn.
• Run cross-customer reviews of recurring quality misses and translate them into standards improvements.
• Partner with engineering on automated validation (schema completeness, duplicates, time sync, metadata coverage, model-assisted review) so the standard is enforceable at scale.

Who You Are

Required Background

• 5+ years working at the intersection of ML and data — annotation methodology, dataset curation, data-centric ML, ground truth design, or labeling-specifications work for autonomy, vision, or multimodal teams.
• Hands-on experience designing taxonomies, ontologies, or labeling schemas that fed production model training (not just internal analytics).
• Strong data instincts: you can open a dataset in SQL, a notebook, or Python and tell us what's wrong with it within an hour.
• Comfortable reading ML papers and translating model-architecture needs into data-structure choices.

Strong Signals

• Built a labeling rubric, ontology, or ground-truth spec that a large annotation org executed against in production.
• Worked directly with research scientists at frontier AI labs or autonomy companies on what training data should contain.
• Background in computer vision, robotics, cognitive science, linguistics, or a related field where taxonomy design is craft.
• Have strong opinions about data quality you can defend with concrete examples.

You Are

• A taste-maker. You believe data quality is a design problem, not a process problem.
• Precise about definitions and obsessive about edge cases.
• Confident saying "this dataset isn't useful and here's why" — to customers, to leadership, to research teams.
• Energized by deciding the standard, not by managing the team that enforces it.

Why This Role

Define the data standards the foundation-model teams shaping the next decade of robotics will train on. Be the person with the pen on what good robotics data looks like — across video, sensors, trajectories, and language. Work directly with researchers at frontier AI labs, not through a sales or PM layer. Build the methodology backbone of a data company at the moment the field is still deciding what "good" means.

What Success Looks Like

• Every major customer program has a clear, documented quality standard, ontology, and acceptance criteria authored by you.
• The data organization runs against a canonical schema and rubric set — not ad-hoc per-project decisions.
• Customer rejection rates fall and dataset usefulness rises because the right data is being collected and labeled the right way the first time.
• Researchers at customer labs treat you as the technical counterpart they want to talk to about what they're actually buying.
• Standards evolve continuously from model-failure signal, not in annual rewrites.