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Guest Post: Advice for Faculty and Industry Interviews

· Abhradeep Guha Thakurta, with commentary by Gautam Kamath

The following is a guest post by Abhradeep Guha Thakurta, on differences between the interview process for faculty and industry research positions in computer science. What follows is his post, and I’ll add a bit of my own perspective at the end.

By Abhradeep Guha Thakurta

My background

I have spent significant time both in industry and academia, and have been on hiring/interviewing panels on numerous occasions in both. I have noticed striking yet subtle differences in the expectations of the interviewers which tend to make considerable differences in the outcome. In the following, I tried to summarize (in my opinion) a few of the critical differences. These thoughts initially originated from a chat thread with a few of my colleagues, which I translated into a more stand-alone document on the advice of Prof. Gautam Kamath.

To bring some credibility to the points I raise, here is my professional background: I have been in the following industry research labs: Microsoft Research (post-doc), Yahoo Labs, Apple, Google Research – Brain Team, and Google DeepMind. Additionally, I have been a tenure-track faculty at the UC Santa Cruz Computer Science and Engineering Department. I have more than 50+ publications, 10+ patents, and 700K+ worth of NSF Grants as PI.

In the following, I will contrast the academic and industry research interviews.

Disclaimer: These are all my personal opinions, and may grossly underestimate the nuances in the process.

Interviews in academia

  1. Qualities on which a candidate is judged: The interviews in academia typically judge a candidate on the following two aspects.
  • Research: The candidate is judged on whether the candidate does interesting research which the peers will appreciate. What appreciation means here are a) Can the colleagues at the university write papers/grants with the candidate, and b) Will hiring this person bring repute to the university. (a) mainly translates to NSF/NIH style grants with dollar amounts associated, and (b) mainly translates to how many top quality research papers the person produces.
  • Teaching: It is a very important part of hiring. Can the candidate articulate their research (to students). This thoroughly should come out from the job talk. The candidate has to articulate the importance of their research to folks who are completely out of domain expertise. As a result, in academia bombing a job talk is mostly the end of the world.
  1. Research statement and teaching statements: They are super lengthy. Most people do not read it. What hiring folks do is skim over it. I got brilliant advice from a mentor of mine, who said “If you want anything to be read by the hiring committee, write it in the first page, and write it in bold. Do not waste space to introduce your research area. Since, if someone does not know your area, they are anyway not going to fight for you.
  2. Job-talk vs 1:1 interviews: Job talk plays a crucial role in the interview process, possibly much more than the 1:1s. The purpose of the talk is to demonstrate three things simultaneously: a) Can the candidate teach, b) Can the candidate explain the research to faculty in allied areas, and c) Does the research have real substance. (a) is judged by whether the talk (at least a half of it) is understandable to a first-year grad student. (b) is judged by conversations during the talk with colleagues from allied areas. Again the objective here is to figure out whether the candidate can articulate their research to a NSF panel to get grants. Finally, (c) is judged by one expert in the area, which typically is the candidate’s host.
    1:1’s are fluid, and the candidate is mostly judged subjectively. However, even under subjectivity, it is necessary that the candidate creates a synergy with at least two senior colleagues who would fight for the candidate in the hiring committee meeting.  One uncomfortable fact is that because of the subjectivity, this is where most of the variance in the hiring process comes from. It is almost impossible to optimize for it beyond getting a few of the basics right. For example, doing research on the interviewer (including asking folks about the personality), being polite and respectful, and trying to relate the research to the interviewer’s work in a genuine way.
  1. Timing for application: Mostly synchronized. Happens at the end of the year, with interviews in Jan to April.
  2.  Repute of the candidate: The hiring has a strong correlation with the repute of the candidate, and how famous the letter writers (and of course what they have written :slightly_smiling_face:). Best letters are the ones which make comparison statements. (E.g., The candidate is comparable to researcher A, who the writer has mentored over the years.) Telling a candidate is a great person is not super important.

Interviews in industry research labs

  1. Qualities on which a candidate is judged: The interviews in industry labs typically judge a candidate on the following aspect.
  • Research: The hiring team wants to see the depth of the candidate’s research and not the breadth. The candidate will be mostly talking to domain experts from very aligned areas of the candidate. The job talk is of a very different flavor than academic one. The best industry job talk is the one that focuses on one problem and goes arbitrarily deep into it.
    • Uncomfortable fact 1: Industry job talks are just conversation starters. Unless the candidate manages to annoy an interviewer present in the talk, it does not count much even if the candidate semi-bombs the talk.
    • Uncomfortable fact 2: Most industry labs interviews nowadays have a coding component (LeetCode type or otherwise). If the candidate fails in those, the chances of getting an offer is fairly low, even if the candidate is stellar researchwise. The reason is labs typically want folks who are generally strong, and can pivot to different areas as needed, and basic general purpose skills are mandatory requirements.
  1. Research statements: Many research labs require research statements and letters of references. They are much less valued in the industry than in academia. In many places they are there for legacy reasons, since the nature of research labs have changed significantly over the years. Of course, a bad letter of reference strongly counts against the candidate.
  2. Job-talk vs 1:1 interviews: In industry job talks are conversation starters, and the focus of the job talk is much narrower. Unlike academic job talks, the sole purpose of the talk for the candidate is to demonstrate the depth of research, which can “wow” an expert in the field. Talking about a problem at great depth is more advantageous than demonstrating breadth. Think of giving an invited talk at a workshop only attended by experts in the field. If a candidate is preparing a job talk both for academia and industry, it is worth preparing two different talks. 
    1:1 interviews are much more structured than academia. There are usually two sets of interviews. One that figures out research depth, and the other that figures out actual hands-on experience with coding (extending from LeetCode to PyTorch debugging.) Research interviews typically are general conversations on research, with a specific problem to be solved at the end (usually in the last 15 mins). Getting a satisfactory answer to this question decides the result of that interview. Coding interviews are standard with correct/incorrect answers. So, there is no subjectivity. Also, they hold a much higher value than a research interview. In both research and coding interviews it is usually safe to be grounded, and not making broad statements. The interviewers that are assigned to Ph.D.’s or post-docs are usually senior people, who can easily tease apart truth from fake.

3. Repute of the candidate: While the repute of the candidate may be important towards securing an interview. It hardly has any impact on the outcome of the interviews. Mostly the outcome depends on the performance of the candidate in the interviews. One crucial aspect of industry research interviews is how the candidate interacts with engineers in the group and in the rest of the organization. Meaning, does the candidate have the appetite and skill to sit down and solve a problem end-to-end, or will just provide high-level advice.

4. Timing: Industry lab hiring happens all the time, mainly based on the requirements. However, once an offer is received it can be delayed pretty much. The reason is both parties (the candidate and the company) know that the employment is at-will. So, it does not make sense for someone to come and leave in a few months with monetary damages to the company. Rather it is best to give the candidate time to choose. There are of course exceptions.

5. Post-doc versus full-time: There is not much of a difference in the industry in terms of job description. Of course one is term limited, no stocks, and lesser pay. If one is looking for an industry post-doc, it is sometimes worth taking a full-time position, and deferring the decision to later.

I (Gautam Kamath) will add some of my own commentary on the academic side of things, given my experience as an assistant professor of computer science at the University of Waterloo in Canada.

I agree with most of what Abhradeep wrote. I diverge slightly when it comes to judging a candidate’s research. To remind, the two conditions that he talked about were the following: a) can the colleagues at the university write papers/grants with the candidate, and b) will hiring this person bring repute to the university. In my experience, the former has not been very important. I personally have never heard discussion on whether a candidate would be able to win grants. This is likely due to a combination of a) being at a Canadian university rather than a US university, and b) being in a computer science department inside a school of mathematics, rather than a computer science and engineering department inside a school of engineering. We also don’t explicitly look for collaborators. On the contrary, if a candidate’s expertise is too similar to others in the department, this may be a reason to pass them over. While collaboration is of course welcome, in the end, faculty are expected to lead their own independent research program. Overall, our goal is to identify and recruit researchers doing field-leading research.


Abhradeep also comments that, in letters, “telling a candidate is a great person is not super important.” I agree, but for me personally, it is valuable that a candidate is a great person. That is to say, besides hiring a researcher, you are also hiring someone who might be your colleague for the next 50 years. I would prefer to hire someone who is willing to help out around the department or group, chime in with their expertise at times when it is needed, and generally work towards making their departmental community a better place. This is always a lower order term compared to research, but being a great person can make me extra excited about a candidate. And certainly, on the other hand, if a candidate has a negative reputation about their personality, this can outweigh the merits of their research.