Human Data Architect, Quality (Robotics AI)

Location: New York, United States; on-site

Salary: $130K–$160K per year plus equity

Company

hirify.global builds data and deployment infrastructure for embodied intelligence, collecting, curating, and licensing robotics training data and deploying robotic systems for enterprise customers.

What you will do

• Define labeling rubrics, severity levels, rejection taxonomies, schemas, and acceptance criteria for customer robotics programs.
• Translate customer and model requirements into precise, measurable, executable data specifications.
• Own taxonomies, ontologies, class hierarchies, temporal segmentation rules, event boundaries, and edge-case categorization.
• Design dataset organization, versioning conventions, schema evolution rules, sampling strategies, balancing rules, and curation principles.
• Build golden datasets, reference examples, and calibration tasks that demonstrate the required standard of correctness.
• Partner with engineering on automated validation and evolve standards based on model-failure signals and recurring customer quality issues.

Requirements

• 5+ years of experience at the intersection of ML and data, including annotation methodology, dataset curation, data-centric ML, ground-truth design, or labeling specifications.
• Hands-on experience designing taxonomies, ontologies, or labeling schemas used in production model training.
• Ability to inspect datasets using SQL, Python, or notebooks and identify quality issues quickly.
• Ability to read ML papers and translate model-architecture needs into data-structure choices.
• Experience building production rubrics, ontologies, or ground-truth specifications for large annotation organizations.
• Background in computer vision, robotics, cognitive science, linguistics, or a related taxonomy-focused field.

Nice to have

• Experience working directly with research scientists at frontier AI labs or autonomy companies.
• Strongly reasoned opinions about data quality supported by concrete examples.

Culture & Benefits

• Quality, trust, and execution are core operating principles.
• The role emphasizes setting standards and methodology rather than managing the teams that enforce them.
• Opportunity to define data standards used by foundation-model teams working on robotics.
• Equity is included in the compensation package.