Teams at AI Security Institute

Research and develop methods for detecting misalignment and loss-of-control risks in frontier AI systems. Build alignment evaluations, conduct pre-deployment testing, analyze model behavior, investigate incidents, and communicate findings to AI companies and governments. The role also involves designing evaluation software and tooling, publishing technical research, threat modeling, and mentoring collaborators. Candidates need substantial AI safety or alignment research experience, strong Python and machine learning engineering skills, and experience with frameworks such as PyTorch or Inspect.

Research Engineer developing AI safety methods focused on human influence. Responsibilities include fine-tuning and post-training LLMs with reinforcement learning, building scalable evaluation and benchmarking pipelines, designing RL environments, applying interpretability techniques, deploying containerized ML systems, and conducting engineering-heavy research on large compute clusters.

Lead and grow the Human Influence engineering function, managing research and software engineers while delivering an engineering roadmap. Design scalable evaluation and benchmarking systems, automated human-AI experiment pipelines, multi-agent study toolkits, data pipelines, reinforcement learning environments, and large-language-model fine-tuning projects. Translate research questions into reproducible production engineering tasks, collaborate with technical and nontechnical stakeholders, and lead projects from scoping through deployment or publication.