About the Role

An early-stage AI infrastructure company is hiring a Research Engineer, QC Automation — the #1 priority hire on the engineering team right now. You'll own end-to-end automation of quality control for AI training data generated by companies using the platform's infrastructure. This is a high-impact, high-autonomy role sitting at the intersection of data engineering, research, and systems design.

You'll be joining a ~15-person engineering group composed of Olympiad medalists, AI startup founders, and published researchers, working on one of the most critical challenges in post-training data quality for reinforcement learning.

What You'll Do

• Automate quality control for training data produced by companies using the platform's infrastructure.

• Build QC systems grounded in true understanding and human judgment — not heavy reliance on LLMs.

• Define and enforce quality standards for post-training datasets.

• Design experiments and metrics to grade agent outputs.

• Partner with data vendors to debug quality issues, diagnose agent failure modes, and improve data generation processes.

• Translate QC learnings into auditing systems, including sampling strategies and rule-based or model-assisted validation pipelines.

• Continuously integrate QC learnings into infrastructure tooling and the data vendor portal to reduce anomalies, inconsistencies, and edge cases.

What We're Looking For

Required:

• 2–4 years of experience in engineering or research roles.

• Proficiency in Python, Docker, and Linux environments.

• Strong understanding of what "good data" means and how to measure it.

• Proven experience building scalable data validation pipelines and automated QA/QC systems end-to-end.

• Experience working on benchmarks and evals — including reasoning about realistic tasks, reliable rubrics, and useful trajectories for RL training.

• Knowledge of statistics and comfort designing metrics, experiments, and QA/QC processes.

• Strong written and verbal communication skills for collaborating across time zones.

• Genuine curiosity across domains and an ability to ask questions that drive understanding.

• Ability to thrive in unstructured problem spaces and work independently in a fast-paced, early-stage startup environment.

Nice to have:

• Background in AI evaluation, reinforcement learning environments, or post-training data pipelines.

• Experience with reward signal analysis or reward hacking detection.

• Prior startup experience or demonstrated comfort with ambiguity and self-direction.

Compensation & Benefits

• Salary: $150,000 – $250,000 USD annually

• Visa sponsorship available for eligible candidates

Location

• San Francisco, CA (on-site) for U.S.-based candidates

• Singapore (on-site) for Southeast Asia–based candidates

• Fully remote as an independent contractor for candidates based elsewhere, particularly in Europe