Researcher, Alignment CoT Monitorability
Researcher designing and running experiments on chain-of-thought monitorability in frontier LLMs to support scalable oversight and alignment. Requires strong empirical ML expertise with LLMs, deep interest in model behavior/alignment/interpretability, and ability to translate ambiguous questions into concrete experiments.
About the job
Responsibilities
- Design and run empirical studies of chain-of-thought monitorability across frontier reasoning models and training settings.
- Build evaluations that measure whether monitors can reliably predict properties of interest, including high-stakes forms of misbehavior.
- Investigate how pre-training, synthetic data, mid-training, post-training, reinforcement learning, and other interventions improve or degrade monitorability.
- Analyze model behavior and turn observations from monitoring into hypotheses, experiments, and recommendations.
- Translate research findings into practical monitoring and oversight approaches that can inform real training runs.
- Collaborate with researchers and engineers across model training, alignment evaluations, monitoring, and frontier-risk work.
- Produce externally publishable research when results advance the broader science of alignment.
Requirements
- Strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs.
- Deep curiosity, interest in alignment, and high agency.
- Depth in alignment, interpretability, model behavior, empirical ML, or adjacent research.
- Ability to turn ambiguous research questions into measurable experiments and follow the evidence when results are subtle or noisy.
- Comfort moving between research ideation and engineering execution.
- Curiosity about multiple approaches to understanding model behavior.
- High independence while collaborating closely across research and engineering teams.
- Care about making increasingly capable AI systems more monitorable, trustworthy, and safe.
Nice-to-Haves
- Direct chain-of-thought interpretability experience.
- Experience with monitoring methods and scalable oversight.
Skills
LLMs, Empirical Ml, Model Training, Model Evaluation, Model Debugging, Reinforcement Learning, Interpretability, Alignment Research, Chain-Of-Thought, Scalable Oversight
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