About the role As a Research Engineer on the Model Performance team, you will help solve one of our greatest challenges: systematically understanding and monitoring model quality in real-time. This role blends research and engineering responsibilities, requiring you to train production models, develop robust monitoring systems, and create novel evaluation methodologies.

All the relevant skills, qualifications and experience that a successful applicant will need are listed in the following description. Representative Projects Build comprehensive training observability systems - Design and implement monitoring infrastructure to keep an eye on how model behaviors evolve throughout training. Develop next-generation evaluation frameworks - Move beyond traditional benchmarks to create evaluations that capture real-world utility. Create automated quality assessment pipelines - Build custom classifiers to continuously monitor RL transcripts for complex issues Bridge research and production - Partner with research teams to translate cutting-edge evaluation techniques into production-ready systems, and work with engineering teams to ensure our monitoring infrastructure scales with increasingly complex training workflows. You may be a good fit if you: Are proficient in Python and have experience building production ML systems Have experience with training, evaluating, or monitoring large language models Are naturally curious about debugging complex, distributed systems and thinking about failure modes Enjoy collaborative problem-solving and working across diverse teams - you’ll work on virtually all stages of our model training pipeline Can balance research exploration with engineering rigor. Have strong analytical skills for interpreting training metrics and model behavior Want to directly impact the quality and safety of deployed AI systems Strong candidates may have: Experience with reinforcement learning and language model training pipelines Experience designing and implementing evaluation frameworks or benchmarks Background in production monitoring, observability, and incident response Experience with statistical analysis and experimental design Knowledge of AI safety and alignment research Strong candidates need not have: Formal certifications or education credentials Academic research experience or publication history Prior experience in AI safety or evaluation specifically We're looking for thoughtful engineers who are excited about the challenge of measuring and monitoring capabilities we're still discovering. This role offers the opportunity to shape how the field approaches model quality assessment while working on systems that will be critical as AI capabilities continue to advance.

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