Data Quality Engineer (AI)

TL;DR

Data Quality Engineer (AI): Engineering robust data pipelines and automated quality frameworks for post-training large language models with an accent on reasoning, alignment, and agentic capabilities. Focus on designing LLM-as-a-Judge systems, establishing measurable data standards, and ensuring high-reliability datasets for critical AI research.

Location: On-site in San Francisco, London, or New York.

Company

hirify.global is an AI research startup building open foundational models and superintelligence with a team of experts from top AI labs.

What you will do

• Own upstream data quality by operationalizing standards for post-training datasets.
• Partner with research teams to translate requirements into measurable quality signals.
• Design and scale automated QA methods, including LLM-as-a-Judge frameworks.
• Build reusable QA pipelines for model training and evaluation data.
• Monitor data quality over time to drive continuous improvement in model behavior.
• Provide actionable feedback to external data vendors based on rigorous QA standards.

Requirements

• Strong engineering fundamentals with experience building data pipelines or QA systems.
• Proficiency in Python and experience with ML/LLM workflows.
• Deep understanding of data quality impact on SFT and RL training processes.
• Experience designing rule-based, statistical, or model-assisted quality checks.
• Ability to own problems end-to-end and collaborate with diverse technical teams.
• Clear communication skills for articulating complex technical concepts.

Culture & Benefits

• Top-tier compensation package featuring salary and equity.
• Comprehensive health, dental, vision, and disability insurance.
• Fully paid parental leave and family planning financial support.
• Daily provided team meals (lunch and dinner).
• Regular off-sites and team celebrations to foster collaboration.
• Relocation support for qualified candidates.