Here is my advice for people interested in research management (RM). It’s an info dump, but you should at least skim all the materials if you are seriously considering this for your career.

What is RM?

• RM means different things in different places. Ensure you check what the work actually is before committing! It also means that the advice here might not be exactly applicable to your situation.
• Fundamentally this is a people-centred role. The most important attitude for an RM is to want and enjoy helping others, as opposed to, for example, wanting to do research. Wanting to do research is not bad, but not primary or necessary. The other skills or knowledge can be learnt (e.g. AI safety knowledge, project or people management), but being inherently motivated by helping people seems innate.
• Copied from my post on why I like RM and MATS:
  • The most common role at MATS, called research manager but I prefer the term research coach, is all about providing 1-1 support to the participants. The participant-mentor relationship is purely based on the research: by default they meet weekly for 30 minutes and only discuss what research has happened, and what research tasks to tackle over next week. The research coach works with the participant on literally everything else, which is broad. Some examples are accountability (e.g. for the research goals, other non-research goals that the participant sets like applying to jobs), interfacing with MATS (so that MATS can track patterns or engagement of participants), people management (e.g. helping with any interpersonal conflicts, or, helping them make the most of the limited 30 minute time slot with their mentor), career planning, general life improvements (a common one is sleep), …
  • What do I like about research coaching?
  • I like to be a jack of all trades and research coaching exposes you to many different skillsets. It has been great to flex and improve many different skills.
  • I like to learn about many different research areas, rather than going deep into one niche sub-sub-question. Working with various participants allowed me to do this.
  • I fundamentally like helping and teaching and coaching people, so the role naturally fits my personality.
  • I do not enjoy the process of doing research myself. I do not inherently find software engineering satisfying and I dislike all the infra stuff. Looks like claude code is almost good enough that I can ignore all that, so maybe one day I will do research via coding agents.
• Cameron Holmes wrote a detailed article on their experience of RM at MATS. Note they are now an RM at UK AISI and the role is significantly different: an example of how the same job title can entail different balance of responsibilities.
• Read the research management sections of this MATS retrospective. Gives an idea of what researchers have found useful from RMs.
• Skim read this detailed guide to RM created by Pivotal.
• RMs are in high demand at the moment, at least at MATS.
• Somebody once asked how being an RM sets you up for future roles. First, I think RM is a great medium- or long-term career in and of itself. There is high skill ceiling across a large range of skills so always more to learn, and you get to have a front-row seat on some of the most interesting research that is happening. Second, given how rapidly AI is improving and how it is changing the nature of work, I think we are at the point where thinking about multi-year career plans does not make sense. Or at least, it is difficult to do this well as it has to take into account the uncertainty inherent in how much society will change because of AI.

Recommended actions to test fit

• Just apply to the role. The application process, at least at MATS, does good job of giving you sense of what job would be like. If you are not good enough, that does not exclude you from re-applying in the future. You might even just get an offer three months down the line: this happened to me!
• There are several ways of getting a taste of doing RM, and part-time so you could do it along side a full-time job. This is biased by my own experience; there are likely other things one can do, but the suggestions below should apply to most people who want to become an RM.
  • The single best is being a mentor for Algoverse’s AI Research Program. You are not expected to be a technical expert (they have dedicated PIs for that) and the primary things you will do align with big parts of the RM role: people management, project management, providing non-expert feedback on research ideas or direction, giving feedback on their conference submissions, etc. They also provide training at the start, a detailed mentor guide, and weekly check-ins to track how you are doing.
  • The next best is being a TA at ML4Good. This helps you practice a complementary range of skills, e.g. teaching or facilitating a range of AI safety topics (e.g. risk models, theory of change, technical topics, governance topics,…), organization, planning, time management, giving career advice,…
  • Then there are range of other things I did that were useful: being a participant in AI upskilling programs like BlueDot or SPAR, facilitating for BlueDot, being a TA at ARENA and ARBOx, leading a project for AISC, managing an intern in my non-AI-safety job.
  • Sign up to Successif. I have not experienced this, but this seems to be best career transition support available. They provide regular 1-1 calls until you make a career change. Probably also worth trying HIP and 80,000 Hours to increase your network and get more high quality perspectives.
• Please provide feedback! Many other people will be reading this so your input - both what was useful for you and what is missing - is invaluable. You can leave a comment here, or send me a DM if you do not want it to be public.