Surprising lessons from my research scientist job search | Yong Zheng-Xin

Surprising lessons from my research scientist job search

Things I wish I knew before.

Published

24 June 2026

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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:

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.

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:

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.

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...