Empirical

The track is defined by its methodology more than by any single research agenda. Fellows run ML experiments to understand and improve the safety properties of frontier models, with work spanning interpretability, AI control, scalable oversight, evaluations, red-teaming, robustness, and model organisms of misalignment. The unifying thread is that progress comes from hands-on work with real models (training, probing, fine-tuning, measuring, etc.) rather than reasoning from first principles alone. This is the largest track in the program and the most common entry point into technical AI safety research.

We are looking for fellows whose primary tool is ML engineering, broadly construed. The essential requirement is the ability to design and run experiments on language models or other deep learning systems and iterate quickly on the results. In practice, that usually means having a solid understanding of Python (with and without AI coding tools), being comfortable with the infrastructure around running models at moderate scale, and knowing which experiments are worth running. Mission alignment is highly important, and fellows should be able to say why a given line of empirical work meaningfully reduces frontier risk, not just whether it yields a successful publication. Educational background and seniority are weighted lightly here relative to other tracks. Past cohorts have included strong fellows ranging from undergraduates to senior industry researchers.

Fellows are matched to mentors based on fit, and projects are scoped to produce concrete artifacts (i.e., papers, evaluation suites, open-source tooling, or technical reports) by the end of the program. The target audiences for the work produced in this track would include safety and alignment teams at frontier labs, governments and other evaluation organizations, and the broader ML research community.

If you are excited by this kind of work, we encourage you to apply.