About the RoleThis is the top hiring priority on a ~15-person engineering team building infrastructure for reinforcement learning environments and post-training AI datasets. As a Research Engineer focused on QC Automation, you'll own the systems that ensure training data quality scales with growing demand - a critical function that sits at the intersection of engineering rigor and research judgment.
What You'll Do- Automate quality control for training data produced by companies using the platform's infrastructure.
- Build QC systems grounded in genuine human understanding and judgment, rather than heavy LLM reliance.
- Define and enforce quality standards for AI training data end-to-end.
- Design experiments and metrics to grade agent outputs across diverse tasks.
- Partner with data vendors to debug quality issues, diagnose agent failure modes, and improve their data generation processes.
- Translate QC learnings into auditing systems, including sampling strategies and rule-based or model-assisted validation pipelines.
- Continuously integrate QC insights into infrastructure tools and the vendor portal to reduce anomalies and edge cases.
What We're Looking For- 2-4 years of experience in engineering or research roles, ideally focused on QC automation or data quality.
- Proficiency in Python, Docker, and Linux environments.
- Proven experience building scalable data validation pipelines and automated QA/QC systems end-to-end without a fully prescribed roadmap.
- Experience working on benchmarks and evaluations for RL training data - including defining realistic tasks, reliable rubrics, and useful trajectories.
- Demonstrated ability to create QC systems based on human judgment rather than defaulting to LLM-based approaches.
- Experience designing experiments and metrics to grade agent outputs, and partnering with data vendors to provide actionable feedback.
- Solid knowledge of statistics and comfort designing metrics and QA/QC processes.
- Strong written and verbal communication skills for effective cross-timezone collaboration.
- Genuine curiosity, intellectual range, and the ability to work autonomously in fast-paced, early-stage environments.
Compensation & BenefitsSalary range:
$150,000 - $250,000 USD annually. Visa sponsorship is available for qualifying candidates.
LocationOn-site in
San Francisco, CA for U.S.-based candidates; on-site in
Singapore for Southeast Asia-based candidates. Fully remote independent contractor arrangements are also considered for candidates based elsewhere, particularly in Europe.
