Notes on the Industry Job Search ← Blog Notes on the Industry Job Search<br>June 20, 2026 For most of my PhD, the job search in my mind was like a sorting hat : senior PhD students would disappear (for several months), then emerge with their fates decided. Even as my close friends began graduating and getting jobs, I knew little about what they were going through apart from the occasional proof of life. When it was finally my turn, I found the process to be far more demanding than I had imagined, and felt like I was learning the rules of the game while playing it.

In retrospect, a lot of my experiences were universal and many of the things I learned along the way now feel like common knowledge. I’m writing this post to share one data point for how the journey can look and hopefully make the job search a little less mysterious for someone in my shoes not too long ago.

A bit of background on me. I applied for Research Scientist / Member of Technical Staff roles at the end of my 6-year PhD in NLP at the University of Washington. I’ve been in school my whole life, and would have loved to be a PhD student forever except that my advisors eventually nudged me to move on. I spent most of my PhD not thinking much about what I would do afterwards, and I was compelled more by working on fun ideas than anything else. This led to a lot of pivoting, but fortunately I managed to keep a consistent thread in my last two years (on tokenization!) because it coincided quite a bit with having fun, and I think establishing an area of expertise helped me stand out in the job search.

My timeline

The figure below shows my job search timeline (inspired by Nathan Lambert’s post), showing interviews as gray icons and outcomes as colored circles. Note ghosted means the recruiter never informed me about an outcome or next steps, and withdrawn means I politely told the company I was no longer interested after receiving some offers I was excited about. In total, I interviewed at 11 companies over 57 interviews. Not pictured are 46 additional recruiter calls and 16 post-offer chats, plus myriad informal networking conversations leading up to the search.

Company order. I decided when to begin each interview process through some combination of whether I felt ready, pressure from the company, how quickly I expected them to move, how excited I was about them, and less-deliberate factors like procrastination. The common wisdom here is to use a few companies for practice, then time the other processes so that all offers are received at roughly the same time for negotiation purposes. While I think this is roughly right in spirit, there are a few considerations I would add.

Practice interviews are helpful, but also recognize that your stamina is finite — be careful not to burn out by the time you get to places you really care about!

There are external factors to timing that are worth taking into account, such as whether the company has headcount and which teams are actively hiring, and this can matter more than your preparation. You can gain some insight into this through your friends and recruiters.

Deadlines come with a lot of flexibility, so offer timing does not have to be very precise. Recruiters recognize you have other processes to finish, and there are various tricks to delay the offer and decision. That being said, there are notorious exceptions (so-called “exploding” offers), so it is important to investigate how much time candidates are usually given to sign.

Getting the first interview. To state the obvious: try to do good work during the PhD, make friends, and collaborate a lot! To get that first interview, sometimes you need to have someone inside the company vouching for you. You can set yourself up for success early on by being social at conferences, collaborating widely, and attending networking events (of course this part doesn’t come easily to everyone — certainly not for me — so take care of your own energy and comfort levels too). During the job search, reach out to people you know (or don’t know) and ask about opportunities. In fact, a big part of the job search is reconnecting with people who you may not have talked to in years — this is okay, expected, and turns out to be a wonderful side effect of the process.

Interview types

I would say there were roughly the following categories of interviews. Overall, technical skills and knowledge are evaluated much more than research experience, though the latter probably gets you the interview in the first place.

ML coding. This was by far the most common. These questions may ask you to implement a given architecture, a decoding strategy, a traditional ML algorithm, or sometimes way more creative things. Being fluent in PyTorch is a must; in rare cases I was asked to use only numpy, for instance when writing the backwards pass from scratch, but I was not expected to be familiar with the numpy syntax.

General coding. Basically LeetCode, sometimes with some extra flavor. It’s...