About the RoleAn 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 ForRequired:- 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
