Member of Technical Staff - Data Quality Engineer (Pre-training)

Summary

Reflection AI is dedicated to building open superintelligence and making it accessible to all. As a Data Quality Engineer, you will ensure that the data used to train models meets high standards of quality and reliability, working closely with researchers to translate requirements into measurable quality signals.

Responsibilities

• Own upstream data quality for LLM pre-training; as a specialist or generalist across languages and modalities
• Partner closely with research and pre-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors
• In addition to human-in-the-loop processes, you will design, validate, and scale automated QA methods to reliably measure data quality across large campaigns
• Build reusable QA pipelines that reliably deliver high-quality data to pre-training teams for model training
• Monitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteria

Skills

• Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for pre-training data
• Detail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data quality
• Solid understanding of how data quality impacts pre-training, with the ability to translate quality concerns into concrete signals, decisions, and feedback
• Experience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-Judge
• Comfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partners
• Proficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable code
• Experience working with large datasets and automated evaluation or quality-checking systems
• Familiarity with how LLMs work and can describe how models are trained and evaluated
• Excellent communication skills with the ability to clearly articulate complex technical concepts across teams

Qualifications

Must Haves

• Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for pre-training data
• Detail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data quality
• Solid understanding of how data quality impacts pre-training, with the ability to translate quality concerns into concrete signals, decisions, and feedback
• Experience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-Judge
• Comfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partners
• Proficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable code
• Experience working with large datasets and automated evaluation or quality-checking systems
• Familiarity with how LLMs work and can describe how models are trained and evaluated
• Excellent communication skills with the ability to clearly articulate complex technical concepts across teams

Benefits

• Comprehensive medical, dental, vision, life, and disability insurance.
• Fully paid parental leave for all new parents, including adoptive and surrogate journeys.
• Financial support for family planning.
• Paid time off when you need it, relocation support, and more perks that optimize your time.
• Lunch and dinner are provided daily.
• Regular off-sites and team celebrations.