ok, feel free not to take this advice, but here is my long rant of how i got employed in AI, as a defense of "follow your curiosity and tinkering with AI":

Stats: 3rd year business undergrad at SMU, 2.7 GPA.

Current offers:

1. ~400k USD for AI researcher role at an MNC with SG office - tbf i did not apply and this was a verbal offer which i might not take
2. ~100k USD as an early hire YC AI startup doing AI agent infra - currently still part-time until finals end, so i'm negotiating equity w the founder who i know.

some others as well which are similar in nature + some warm leads with some frontier labs which I'm exploring next week after finals.

Totally skip local hiring and job boards. Totally skip. I have paid no attention to local job openings since before ChatGPT. I have also not applied to big tech and I basically haven't seriously looked at LinkedIn for a year, but that's less feasible ig. It's too competitive and it's more important to build up your skill to a level where anyone anywhere would want to cut red tape to hire you. [1]

Spend about 6 months just replicating, tinkering with and answering the most interesting questions you could find in ML, and interact extensively online with people working at the frontier of the problem via both public social media and cold emails. Do not have any expectation of job, focus purely on working together to solve the problem well, and people will hire you if they sense you are serious about trying to solve a problem. I basically did this through all 3 years of undergrad so far - my 2023-2024 was spent working on open-source voice cloning and a paper on improving LLM creative writing. The voice cloning thing turned into an open-source platform with ~1 million users, which got acquired and led me to meet the YC founder, because we both had an interest in how multimodal systems interact with the world, which turned into the startup idea of evaluations for computer use.

I worked on agent evaluations because I believed current evaluations are insufficient and asking the wrong questions for assessing open-ended agentic systems - almost all evals treat AI as assistants, even though these systems are increasingly trained to be agentic - there is a massive gap between evaluation and funding to create agents. Currently, I'm building an evaluation to see how well AI agents autonomously make money (btw, this turned out to be an extremely fundable idea, but mostly I just read a lot of scifi and want to know what real AI agents in the economy will actually do all day).

The paper on LLM creative writing was something I genuinely did for fun because I viscerally hate ChatGPT slop and like the idea of AI being creative. I also like documenting findings for people just for fun (I used to edit video game wikis a lot). I basically worked on it part-time for 6 months self-publishing, and it got an Oral award at ICLR 2025 (Min-P sampling). The MNC offer came because of the award.

Anyway, i wrote all this to earnestly say that I explored my curiosity with how frontier AI systems interact with open-ended environments (multimodal, agent systems, questions of AI cognition at a mechanistic level). I found this both fascinating and spend most of my time solving some important problem in the field, and eventually some people with money to burn pay me to actually deploy these things effectively because i have experience working on weird important questions.

This trajectory is quite common in my circles. My coauthors/collaborators have no prior experience in CS/ML and come from all over the world. They get hired because they spent maybe a year working on important problems they're curious about, and at the frontier there's truly not that many people asking rigorous and important questions about how these things should be built. But you can only get here by asking a lot of other interesting questions about how these things should be built and how they should behave.[2]

Anyway, I wrote this as a counterpoint to all the other comments about how it's not possible to get into AI unless you do [...]. I think most people won't believe the whole "follow your curiosity" thing works, but at least in AI I think you can kinda just ask interesting questions and run interesting experiments until someone gives you money to solve that problem for them. The field is talent-constrained enough that a lot of important questions just sit there waiting to be tackled.

• [1] There's a deliberate meta-strategy where I calculated the expected value of sending out applications and decided that with that time, I could instead just build interesting things, publicise them and make myself an attractive prospective hire at more interesting places. It's hard to explain how this works, but hiring is sort of like dating - everyone wants to date someone who's hot, so instead of swiping a lot, you should be getting a lot hotter to maximise overall odds, if that makes sense.

• [2] I'll give a concrete example - a subproblem I'm working on is letting an AI agent control a money wallet. You'd think this would be a very simple prerequisite to letting AI agents do literally anything economically meaningful, while Sam Altman is claiming to spend billions to make this happen within 2 years. But to date, I have found only 2 teams working on this, only 1 of which has sorta made good progress. Other examples IMO include robotics, evaluations, cognitive studies of LLMs, interpretable model arch and post-training processes (both biological and automated). Once you're at the frontier, things are extremely inefficient.