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

Highlights

• Own upstream data quality for LLM pre-training across languages and modalities.
• Design and scale automated QA methods to measure quality across large campaigns.
• Turn fuzzy ideas of good data into measurable standards shaping model performance.

What they offer

• Top-tier compensation: salary and equity structured to recognize and retain talent globally
• Stock options for everyone who contributes to Reflection's success
• Comprehensive medical, dental, vision, and life insurance plus annual wellness allowance
• Lunch and dinner provided in the office daily
• 22 weeks paid parental leave for all new parents, including adoptive and surrogate journeys
• Unlimited paid time off in the U.S. and 30 days in the U.K.
• Regular off-sites, happy hours, and team celebrations

Ideal candidate

Detail-oriented engineer curious about data quality and its impact on frontier models. You enjoy owning ambiguous problems end-to-end and partnering with researchers to turn quality insights into better training data.

Requirements

• 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 ability to translate quality concerns into concrete signals and decisions
• 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 with researchers, engineers, and operations partners
• Proficiency in Python and building ML / LLM workflows, comfortable debugging and writing scalable code
• Experience working with large datasets and automated evaluation or quality-checking systems
• Familiarity with how LLMs work and how models are trained and evaluated
• Excellent communication skills with ability to clearly articulate complex technical concepts across teams