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 studies scope generalisation in post-training: why similar narrow training signals can produce a broad change in behaviour, a local strategy, or a response tied to one particular setting. Fellows will work with controlled model organisms, comparing variants of SFT and RL, and tracking how behaviour and internal representations shift during training.
I have two broad areas.
Security:
I am interested in building demonstrations for hacking real-world AI deployments to show that they are not secure. The goal is to force companies to invest in alignment techniques that can solve the underlying security issues.
Verification:
Verification via TEEs or ZKPs
This stream will focus on model motivations and character, open-ended environments, and new forms of misalignment.
At Fourth Eon Biosecurity we're building adaptive, AI-native safeguards across the bioengineering stack, with a focus on function-based DNA synthesis screening. Fellows in this stream will work on technical research projects at the intersection of AI safety and biosecurity, aimed at reinforcing screening and generalizing detection beyond known threat signatures. Projects span mechanistic interpretability of bio foundation models, model evaluations for biosecurity-relevant capabilities, and agentic sequence analysis workflows.
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.
My stream aims to work on multi-agent systems, AI control red-teaming, eval design, and propensity studies.
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.
This stream focuses on building companies in AI assurance infrastructure, from energy and physical networking to authentication and interpretability.