Research Engineer

Responsibilities

• Identify data quality issues including inconsistencies, formatting problems, and ingestion challenges
• Perform initial manual data quality review to deeply understand failure modes
• Build systems to automate quality checks at scale using rule-based and AI-driven approaches
• Design hybrid systems that balance automation with human-in-the-loop review where appropriate
• Continuously improve verification methods as the data landscape and AI tooling evolve

Requirements

• Deeply technical, with a strong learning slope and the ability to ramp quickly in a fast-moving field
• Background in AI/ML engineering, or software engineering at an AI-focused company with visible data ingestion and processing experience
• Able to reason about likely data quality problems from first principles
• Comfortable owning ambiguous, open-ended problems end to end
• Comfortable working in person, full-time, in a San Francisco office

Qualifications

Bonus: experience working with noisy or unstructured data, or judgment on when to use automation versus human-in-the-loop review