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 works on character training for language models, and on research to understand the open model ecosystem, including policy-facing work.
Neel takes a pragmatic approach to interpretability: identify what stands between where we are now and where we want to be by AGI, and then focus on the subset of resulting research problems that can be tractably studied on today's models. This can look like diving deep into the internals of the model, or simpler black box methods like reading and carefully intervening on the chain of thought - whatever is the right tool for the job. This could look like studying how to detect deception, understanding why a model took a seemingly concerning action, or fixing weak points in other areas of safety, e.g. using interpretability to stop models realising they are being tested. You can learn more about Neel's approach in this podcast.
He has spent far too much time having MATS scholars, and has worked with ~60 so far - he’s excited to take on even more!
Projects in this stream will be on AI welfare and moral status; more specifically, on what it takes to be a moral patient and how we can determine whether AI systems meet the conditions. I'm looking for applicants who have ideas about these topics and are motivated to explore them in more detail.
In this stream we will explore extensions and implications of our discovery that neural networks pretrained on next-token prediction represent belief-state geometry in their activations, decompose their world into parts, and discover abstractions. We will build on this fundamental theory of neural network representations in order to discover the building blocks of cognition.
The Redwood Research stream is looking for fast empirical iterators and strategists to work on control research.
Roger Grosse’s stream investigates how to improve influence functions and other training data attribution methods, and uses these tools to study alignment-related phenomena such as out-of-context reasoning and emergent misalignment. The ideal scholar has experience with LLM internals, strong statistics/applied math skills (especially numerical linear algebra), and can independently drive research from literature review through experimentation and analysis. Roger provides shovel-ready projects while giving exceptional scholars freedom to pursue their own ideas, and is open to scholars collaborating with others.
Backing projects focused on product development and organization building in the areas of AI safety and alignment, biosecurity, and critical cybersecurity. Looking for fellows who are self starters, default to action, and have a desire to create.
In the shard theory stream, we create qualitatively new methods and fields of inquiry, from steering vectors to gradient routing to unsupervised capability elicitation to robust unlearning. If you're theory-minded, maybe you'll help us formalize shard theory itself.