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 getting hands on real models (training, probing, fine-tuning, measuring) 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 strong Python (with and without AI coding tools), comfort with the infrastructure around running models at moderate scale, and enough research taste to know which experiments are worth running. Mission alignment matters: 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 by program end: papers, evaluation suites, open-source tooling, or technical reports. Target audiences include safety and alignment teams at frontier labs, governments and other evaluation organizations, the broader ML research community.