5 дней назад
Head of Central Quality and Project Enablement (AI Data Services)
140 000 - 180 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Head of Central Quality and Project Enablement (AI Data Services): Building standardized enablement packages, quality pipelines, and delivery controls for complex human-data and annotation projects with an accent on sampling methodology, agreement metrics, and workflow design. Focus on translating ambiguous customer specifications into reliable guidelines, diagnosing quality drift, and scaling reusable QA playbooks across Data Services engagements.
Location: San Francisco or Austin, Texas, United States
Compensation: $140,000–$180,000 per year
Company
provides human-data services and Label Studio, an open-source platform for data labeling and evaluation used by AI teams.
What you will do
- Translate customer specifications into annotator guidelines, decision trees, edge-case libraries, qualification tests, and onboarding materials.
- Run pilot and calibration rounds, resolve specification ambiguities, and maintain versioned project guidelines.
- Design project-specific quality plans using gold tasks, overlap, consensus, multi-stage review, and expert adjudication.
- Define sampling methods, confidence targets, agreement metrics, error rates, and acceptance thresholds.
- Configure quality workflows in the labeling platform and develop reusable quality playbooks.
- Own final quality sign-off, monitor delivery quality, perform root-cause analysis, and produce quality reports.
Requirements
- 6+ years of quality assurance or quality operations experience in data labeling, human data, or ML training and evaluation data.
- Experience leading a quality function or team.
- Ability to convert complex or ambiguous specifications into effective guidelines and training.
- Strong applied statistics skills, including sampling design, confidence intervals, and agreement metrics.
- Hands-on experience configuring review and QA workflows in an annotation platform.
- Proficiency in Claude Code and strong written communication.
Nice to have
- Experience with LLM, RLHF, or preference-data projects.
- Experience managing expert or domain-specialist workforces.
- Instructional design background.
- Familiarity with Label Studio or model-assisted QA.
Culture & Benefits
- Work on real-world data collection, multimodal pipelines, and multi-step workflows for AI products.
- Own quality standards and enablement across Data Services engagements.
- Build scalable quality reports, templates, and playbooks.
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