Data Quality Engineer (Post‑training) – Member of Technical Staff

About the role

Data quality is critical for advancing AI models. As a Data Quality Engineer on the post‑training team, you will ensure that the data used to train and evaluate large language models meets high standards of reliability and impact.

Key responsibilities

• Own upstream data quality for LLM post‑training and evaluation, translating research requirements into measurable quality signals.
• Design, validate, and scale automated QA methods, including LLM‑as‑a‑Judge frameworks, to assess data quality across large campaigns.
• Build reusable QA pipelines that deliver high‑quality data to post‑training teams and monitor quality over time.

Required profile

• Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for post‑training data.
• Detail‑oriented analytical mindset able to identify failure modes and subtle data issues.
• Comfortable working autonomously and collaborating with researchers, engineers, and operations partners.

Required skills

• Proficiency in Python and building ML/LLM workflows.
• Experience with large datasets and automated evaluation or quality‑checking systems.
• Familiarity with LLM training, evaluation, and concepts such as SFT and RL.

What we offer

• Top‑tier compensation with salary and equity.
• Stock options for all contributors.
• Comprehensive health, dental, vision, and life coverage plus wellness allowance.
• Unlimited paid time off in the U.S. and generous parental leave.
• Meals provided daily on‑site and visa sponsorship support.