Product Manager (AI)

Location: Remote

Company

hirify.global develops a secure, efficient, and privacy-centric platform for exchanging AI training data.

What you will do

• Own the supply-side ingestion platform that transforms raw partner data into trustworthy, catalog-ready datasets.
• Define ingestion stages, validation gates, quality checks, metadata requirements, and catalog-readiness criteria.
• Lead product decisions for metadata generation, including transcripts, tags, confidence scores, schema inference, storage, and surfacing.
• Define QA standards and build tooling that enforces them; write SQL and review pipeline outputs directly.
• Translate healthcare, media, and other vertical requirements into consistent platform standards without custom engineering for every deal.
• Own the roadmap for ingestion quality, QA tooling, de-identification workflows, and catalog readiness in collaboration with engineering, vertical stakeholders, GTM, delivery, and DataLab.

Requirements

• 4–7 years of product management experience owning a data pipeline, data quality system, or data ingestion platform.
• Hands-on technical depth with SQL, pipeline logs, schema mismatches, and data validation architecture.
• Experience ingesting messy, inconsistently formatted data from external or third-party partners and making it trustworthy.
• Experience making build-versus-partner decisions and evaluating vendors in changing technical environments.
• Ability to write platform requirements and communicate effectively with engineering teams and vertical product managers.

Nice to have

• Experience with data quality frameworks, metadata standards, or catalog tooling such as dbt, Great Expectations, or data contracts.
• Familiarity with de-identification approaches for PHI, PII, or confidential enterprise data.
• Background in healthcare data operations, financial data infrastructure, ML training pipelines, AI data workflows, or data governance.

Culture & Benefits

• Lean, fast-moving, high-trust environment focused on velocity and measurable impact.
• Culture of ownership, resourcefulness, integrity, kindness, candor, and shared accountability.
• Work alongside teams building infrastructure for AI training data and partnerships with ambitious AI organizations.