Surprising lessons from my research scientist job search

There are two recent blog posts from Alisa and Silvia, both CS PhD students, on how they prepared and got into frontier labs such as OpenAI and Google Deepmind. I highly recommend them, and after seeing the reactions on Twitter, I want to share a different angle: what surprised me during my own research scientist job search.

I write this post for two primary audiences:

1. CS PhD graduates who are probably like me, who spent 5-6 years working on multiple research papers and now trying to look for industry opportunities.
2. AI safety fellows who are applying for full-time positions.

Disclaimer: no LLMs were used in the writing.

Personal Experience

I am a fifth-year PhD student at Brown University. My job search experience is a bit unconventional as I did some research pivot during my last year of PhD.

In Fall 2025, I was applying for multilingual and AI safety positions, but mostly receiving research scientist opportunities for multilingual/post-training. This is because of my research portfolio having less work on core AI safety topics.

I decided during the semester that I needed to fully pivot to AI safety research because I think there are a lot of important areas within AI safety that need immediate attention as we are approaching AGI/ASI. So when I received the Astra Fellowship, I decided to take a few month break from job search and focused on doing the fellowship well such that I am more qualified for higher-impact AI safety roles. To this end, I turned down existing offers and pushed back my graduation to 2027.

Nearing the end of my fellowship, I restarted my job search and things went a bit more disorganized than what I originally had in mind. My original plan was to finish my fellowship by June, turn my work into a paper, and start my interviews (which means I would have only started my interviews in July). However, due to timing reasons ¹ and my worry about headcount, I started in around mid-May and got offers that I am excited about before mid-June. I actually withdrew from some ongoing interviews and did not even have the chance to fully explore my options.

All in all, I am glad that things worked out so I did not have to deal with the funding issue (as I pushed back my graduation) and the anxiety of continuous job search (at least for the near term, hopefully). No words can describe how grateful I am for all the people who supported me during the process.

Surprise 1: Only one or two papers really matter during my job search.

Based on Alisa’s post and reactions, perhaps many already know that the interviews (e.g. LeetCode) may not be related to the research work you’ve done.

I would take this even further and said that only one or two papers really matter during the job search. Sometimes, none at all, and I was just being evaluated on how well I solve the team’s problems on the spot.

In my experience: the roles of the papers are mostly two:

1. Get my foot in the door. I have worked on something that the team liked, or my paper has demonstrated certain expertise the team is looking for, so I am now put into the interview pipeline. That is, I just passed the bar and now I’m officially being considered as an applicant.
2. Deep dive. This happens usually during research presentation or research discussion, where I talk about the motivation and the details behind one work. Sometimes, such presentation can be as short as 20 minutes only.

So to some degree, the volume of publication does not really matter aside from establishing credibility. In my case, my multilingual research papers substantially outnumber my AI safety papers–––but given my pivot to AI safety research, none of those work have any bearing on my interview outcome, including papers I received best paper awards on.

This is actually liberating, because that means that you can always pivot to a new field that you think is impactful and still get dream offers if you demonstrate sufficient expertise in that field and the team wants you. On the flip side, it also suggests that you’d need to keep up with the field, as past success has less bearing on whether you’d get hired into new opportunities.

Surprise 2: Very diverse interview rounds.

I originally came into the interviews expecting something like how fresh-grad software engineers being interviewed (e.g., Leetcode-style questions and behavioral rounds) plus some technical rounds about LLM/deep learning.

That there’s something standardized about the interview rounds–––for which I believe the blogs from Alisa and Silvia give the impression of.

Surprisingly, I have received questions about system design as well as parallel programming (such as using asyncio to implement concurrency operations) during my job search. I also learned that there are interview rounds where you are evaluated on how well you use AI agents. All in all, the lesson here is that you should always expect wildcard questions and diverse interview rounds.

Surprise 3: Work trials.

This is something entirely new to me. It was also surprising for me when I saw that in Alisa’s post since I thought work trials are only common for AI safety positions. Apparently, it is increasingly common for AI startups as well.

Work trials are completely different from onsite–––you are not flown to the company to do multiple interview rounds onsite; instead, you are working with the team to solve a task. Sometimes, the task can be open-ended.

These work trials are paid usually, but what surprises me is that some of these in-person work trials can last up to a week.

For me, doing work trials make it really hard to prepare for other companies’ interviews as I would have to put in my everything on the current task assignment and have no bandwidth for interview prep with other companies. This is something you should be mindful of when you schedule interviews, especially if you are interviewing with multiple companies simultaneously and have tight turnaround time.

Surprise 4: Timing matters a lot.

In this current job market, timing plays a substantial role.

For instance, in the last Fall, it was extremely challenging to find AI safety positions compared to positions related to RL. But now, there are more startups offering opportunities related to AI safety (such as Lila and Mechanize).

There are a few discussion points about how timing affects your search for full-time positions:

1. Your work got viral, and a lot of orgs take interest in your work and want to recruit you. You might get caught off guard by the timing, and the best thing you could do here is to take advantage of that timing and go through the interviews.
2. Your area of research is becoming more popular. This is related to the AI safety example I mentioned above. You can assume that the opportunities are generally more available. The job application windows can be as short as under a month, or can span several months as the companies are trying to grow.
3. Headcounts. This is something you should ask your recruiters about especially if you are planning to postpone interviews or doing some meta-planning about how to simultaneously interview with multiple companies.
4. Exploding offers. If you were in this scenario, ask other companies to accelerate interviews. Don’t be surprised if you have to have three back-to-back interviews within a single day, and you only have less than a day to prepare for them.

It is reasonable to ask to start the interviews later (like you can push till one or two months later), but usually once you have begun the interview, the intervals between each round are usually short. Another related note is that some positions expect you to start the role in the next month or two, though the start date can be negotiated.

Surprise 5: Return offers are rare.

Compared to software engineering positions, where return offers are usually a norm, for research roles it is more case-by-case.

For instance, during my Meta internship in 2024, return full-time offers were rare and highly headcount/team-dependent. Many of my friends did not get it. For my Astra fellowship with OpenAI, I still had to go through all the interview rounds to get into OpenAI just as any other applicant.

I heard that there are some other organizations where the interviews are more expedited; for instance, if team match works out, you just need to go through one or two more rounds.

Surprise 6: A lot of interviews are not [your-topic]-related.

This came as a surprise to me because I was pivoting from doing capability research (multilinguality) to safety, and I thought safety-related interviews would take a big portion of full interview pipeline. This impression was amplified by how much AI safety discussion was constantly held within Constellation during my Astra Fellowship.

That wasn’t true.

In fact, I have encountered many rounds not related to AI safety at all, let alone related to my research interest. I believe this experience is similar to what’s shared by Alisa and Silvia (even though they work on other AI fields).

In a handful of places, it still felt like I was evaluated on how well-rounded an AI researcher I was. I believe there’s merits to this (e.g., field is fast-moving so checking on fundamentals is important, etc.), but I was definitely expecting a higher ratio of AI-safety-related questions since it is in my opinion an urgent research topic, and it is still a rather niched field. Perhaps my interviewing experience might be more different for senior positions.

For safety researchers: If it is helpful for you, I have co-written a LessWrong post about the safety-specific rounds, but expect a lot of diversity in the questions asked.

Reading Resources I recommend (2026):

The following are resources I can vouch for on how to prepare for the job market.

1. Nathan Lambert – Thoughts on the job market in the age of LLMs
2. Alisa Liu – Notes on the Industry Job Search
3. Silvia Sapora – ML Job Interviews: The Ultimate Guide

---