Image by Javier Allegue Barros (via Unsplash).

‘Tis the season of job searches for many people fresh out of gradschool and college, and for many that just finished/are about to finish a PhD degree, the momentum and curiosity built up over the last few years typically encourages one to continue pursuing research at some capacity either within academia or the industry.

One of the biggest questions many ask themselves at this junction is, “Would academic research jobs better suit my goals, or industry ones?”

To answer this question, it’s helpful to first understand what these jobs entail. I asked Google, and Google’s Generative AI gave me a pretty solid generic answer:

Google Generative AI answering "differences between academic and industry researchers".

This, along with many answers in this Quora question and many top Google results actually cover a lot of the important first-order differences that are quite accurately applicable to computer science/artificial intelligence, which I will focus on.

So what is it about this post that these online resources don’t already cover?

Looking back on 3+ years working as an AI researcher in the industry, split between one “pure research” and one “applied science” position, I realized that there is some unobvious learnings I wish I had known more about when I started. Hopefully, this can be useful to some other early-stage researcher facing similar choices at the start of their career.

Zeroth, be scientifically skeptical about anecdotes and advices (online or from trusted personal sources). When someone says “A is better than B in X”, understand that, just like in research papers, this means when averaged over the examples observed by that person in their situation and experience, A is on average better than B in X by an undetermined amount (unless otherwise stated). Remember this does not necessarily apply to you or your situation. Always keep and eye out for other evidence that can help you reinforce/disprove this statement.

First, both academia and industry have opportunities to contribute with your expertise other than doing first-hand research. This is often not said enough given where many PhD graduates typically go, but there are many administrative, engineering, consulting, product management, entrepreneur, etc. opportunities where you can leverage your unique research background, knowledge, expertise, and/or training in the job market. People too often say they wish they had someone in a position they work with that could understand how research works or how best to work with researchers, yet too few actually realize that they could be that person.

Second, like most PhD students in training, early-career industry researchers are mostly independent; unlike most PhD-to-be’s, early-career professorship involves administration and managerial tasks from Day 1. Regardless of how much or how little you have worked with groups or mentored others during gradschool, most fresh PhDs enter the industry research workforce as independent contributors from the start. With some help from more experienced colleagues, you typically need to independently drive some research/applied projects (with the exception of mentoring research interns) before you prove yourself ready to accept bigger challenges, and start hiring/leading a small team to do research. During these first years, you need bide your time and convince the world that you are capable as an independent industry researcher, before you are trusted to build larger things with more people.

On the contrary, professorship typically starts with new administrative and managerial challenges from the beginning. Planning budget, applying for grants, hiring students, purchasing equipment, designing and teaching classes are typical challenges that are difficult to prepare for in advance during one’s PhD career. Similarly, there is usually a support system with more experienced people around to help, and I have heard good things about postdoc positions being good at preparing one for these challenges.

Third, good engineering makes a bigger difference in the industry. Many good researchers are not good engineers, and this applies to many folks that graduated from great Schools of Engineering across the world. As an industry researcher, however, your research work will more often than not scale to beyond you and a handful of people you work closely with, and survive beyond the typical months to years it takes to generate a research paper. While there are good engineering projects that originated from academia (if I may say so myself), someone usually needs to go out of their way to maintain it and keep it current, which is difficult for PhD students if no groundbreaking research is associated with it. In the industry, however, this can be a basic requirement especially in cases where job freedom means new people need to be onboarded to continue large research efforts and experience people might disappear at some point.

Last but not least, research jobs in the industry come in many flavors. And no, it is not just two flavors with one being pure research and the other being purely product-driven, or a static mixture of the two. It is also uniquely what you define it to be – no two people arriving at virtually the same position will explore it in exactly the same way, usually influenced by their background, current interests, aspirations, and life circumstances. That said, there are frameworks and patterns to match when approaching these, which I hope to cover more in my next post. For now, I think a useful question to ask your interviewer when searching for prospective positions (and to think for yourself), is “What does your typical week look like, and do you typically plan out your work for a quarter or a year?” This will both help you get a glimpse of the day-to-day, and a peek into the near future and what processes are in place.

Before I end this post, I would like to part with a reflection on a key similarity between industry research jobs and academia: they are both real world jobs, with which comes a lot more responsibility to other people. This can mean a financial responsibility to support others, a personal responsibility to mentor or lead, a collaborative responsibility to be a functional part of a larger team, and/or a punctual responsibility to uphold time commitments to people or teams that depend on you. Gone are the days when slipped deadlines always had some other deadlines on their heels, when your decisions only impacted you and your work, or that there is always your mentors to lean on in all situations. It can be stressful and uncomfortable, yet that is the feeling we are all familiar with by now after years of research training – the beginning of an exciting new journey.