> Markdown version of [/jobs/ext/494094-human-data-architect-quality](https://www.wearedevelopers.com/jobs/ext/494094-human-data-architect-quality). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Human Data Architect, Quality - **Company:** MECKA ASSOC - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $130,000.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Computer Vision, Cognitive Science, Data Architecture, Data Dictionary, Data Structures, Python (Programming Language), DataOps, SQL Databases - **Published:** June 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5ec57e32b5d7eecd ## About the Role Do you have experience in Schema design?, * 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., * 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. ## Description 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., * 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., * 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., * 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., * 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. ## Related Videos - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1520-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Exploring 5 Key Applications of AI Abundance with Blockchain Assurance](https://www.wearedevelopers.com/videos/971-exploring-5-key-applications-of-ai-abundance-with-blockchain-assurance) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [The future is here: How artificial intelligence is influencing UX design](https://www.wearedevelopers.com/magazine/65-the-future-is-here-how-artificial-intelligence-is-influencing-ux-design)