Empirical
Streams in this track include hands-on research using machine- learning experiments to understand and improve model safety including AI control, interpretability, scalable oversight, evaluations, red-teaming, and robustness. This track is defined by its methods rather than any single research agenda. If your primary tool is ML engineering, this is the track for you.
- Stage 1: Complete the general application
- Stage 2, phase 1: Complete 1–2 assessments evaluating research taste and technical implementation skills, alongside possibly reference requests
- Stage 2, phase 2: Stream selection questions
- Stage 3: Interviews and work-tests
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.
This stream will focus on model motivations and character, open-ended environments, and new forms of misalignment.
This stream will focus on monitoring, stress-testing safety methods, and evals, with a focus on risks from scheming AIs. Examples include (black-box) AI control techniques, white-box monitors (probes etc.), chain-of-thought monitoring/faithfulness, building evaluation environments, and stress-testing mitigations.
I'm open to quite a broad range of work. By default I expect scholars to work on reasonably conceptually-simple empirical projects, such as building capability or propensity evals.
I'm interested in better understanding and controlling how post-training causes alignment-relevant behavior. This is a pretty broad area, and I’m open to many approaches to these problems! Potential areas of study / methods of attack might include model organisms, training run science/ablations, root causing strange behaviors, or studying how best to robustly induce behaviors or values or beliefs into models.
If you're a technical researcher who's been wondering if your work should be a company, this stream is for you.
I invest in AI safety and assurance startups at pre-seed, and I'll treat fellows the way I treat founders I'm considering backing: real diligence, honest feedback, introductions to people building in the space. The output should be a defensible answer to 'should this exist as a company, and am I the person to build it?
We are excited to supervise projects:
1. Study the causes and implications of situational awareness, and the role that meta-cognition plays in (multi-agent) alignment;
2. Contribute to LawZero's Scientist AI, in the form of contextualization and uncertainty estimation.
Lee's stream will focus primarily on improving mechanistic interpretability methods for reverse-engineering neural networks.
This stream focuses on critical challenges in AI safety and alignment, including risks from automating AI research, bottlenecks to recursive self-improvement, and the automation of safety and alignment research. Priority topics also include AGI privacy, measuring long-horizon agentic capabilities, developing new alignment methods, and advancing the science of post-training.