Data Quality Engineer for LLM Pre-Training Pipelines

Sierra Ventures; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Top-tier compensation

Stock options

Health & wellness (medical, dental, V

Meals in office

Parental leave 22 weeks

Unlimited PTO (US)

Visa sponsorship

Job summary

Reflection is seeking engineers to own upstream data quality for LLM pre-training, partnering with researchers to translate quality requirements into measurable signals. You will design automated QA, build scalable data quality pipelines, and monitor quality over campaigns, collaborating with researchers, engineers, and operations partners.

The role emphasizes strong Python skills, data pipeline experience, and the ability to communicate complex data quality concepts across teams.

Qualifications

• Proficient in Python with ability to build scalable ML/LLM workflows.
• Experience designing and validating automated quality checks (rule-based, statistical, or model-assisted).
• Strong data-pipeline thinking and debugging of data quality issues.
• Ability to translate quality concerns into concrete signals and feedback for teams.
• Excellent cross-team communication to explain complex technical concepts.

Responsibilities

• Own upstream data quality for LLM pre-training across languages and modalities.
• Translate requirements into measurable quality signals and provide feedback to external data vendors.
• Design, validate, and scale automated QA methods for large data campaigns.
• Build reusable QA pipelines delivering high-quality data to pre-training teams.
• Monitor and report data quality over time, driving continuous improvement.

Skills

Python

ML workflows

Data pipelines

QA systems

Quality checks

Communication

Job description

Reflection is seeking engineers to own upstream data quality for LLM pre-training, partnering with researchers to translate quality requirements into measurable signals. You will design automated QA, build scalable data quality pipelines, and monitor quality over campaigns, collaborating with researchers, engineers, and operations partners.

The role emphasizes strong Python skills, data pipeline experience, and the ability to communicate complex data quality concepts across teams.