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

Data is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data.

As a member of the Data Team, your mission is to ensure that the data used to train our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities.

Working with world-class researchers on our pre-training teams, you'll help turn fuzzy notions of "good data" into concrete, measurable standards that scale across large data campaigns. We're looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model performance.

- 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

- 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