How crucial is a PhD in AI for securing a job within the industry? | Hacker News Reader

How crucial is a PhD in AI for securing a job within the industry?

6 pointsal_levin3 comments

29M. I'm currently doing a PhD in AI, which is going fine, but on a niche subject (nothing related to generative AI, Computer Vision, or Deep Learning). I still have two years left, and unfortunately I came to the conclusion that academia is not for me, and I would like to leave. I enjoy doing research, but I hate feeling trapped in an academic bubble of papers and conferences with no real-world ramifications (great respect for fellow researchers, but it's just not for me).

The consensus seems to be, "if you want to do a PhD for the money (my case), don't do it". But at the same time, I have seen people suggesting that the bar is rising and that having a PhD can help you get a high-profile job.

What is not clear to me is how many doors I would close. Realistically, it's now or never. I am afraid that if I change my mind in 2 or 3 years, I would be too old to start a PhD again. On the other hand, continuing is really stressful and damaging to my mental health (I've had depressive episodes in the last year), and, financially I'm barely breaking even.

Happy to chat and provide recommendations. I only have a masters, and worked as an ML Engineer in the industry for about 5 years now. It was tough to get a job, but really good portfolio of training and deploying models can really help you stand out. I have teammates with PhDs in Bioengineering and non CS PhDs, and they excel in the industry. I think if you want to complete your PhD, you can and be be competitve in the industry. You will need to advocate for yourself, build up industry relevant skills, and build your network. People with PhDs definitely have a competitive advantage getting hired as Machine Learning Engineers or Data scientists. Research Scientists can be more competitive. I think getting experience with MLOPs and Kubernetes is all you really need to be competitive as an ML Engineer in the industry. I recommend reading this book: https://www.oreilly.com/library/view/machine-learning-interv... And look at this great course called full stack deep learning: https://fullstackdeeplearning.com/course/2022/

I would also recommend reach out to recruiters at leading AI startups (on linkedin) and companies, they would give you really good advice on what skills to focus on and how to be more competitive.

I hired and worked with many PhDs in big tech. The need for them is somewhat cyclic. More precisely, they are in demand when a new technology becomes important that few outside of academia know. That’s AI now, distributed computing before that, alternative (NoSQL) databases before that. After awhile (if it isn’t a fad), these niche areas become mainstream and you can find people who are AI programmers, or distributed systems engineers, who learned as undergraduates or on the job and the PhD jobs go back to being more research. I do feel a PhD does not automatically make you overqualified as much as it did in the 80s or 90s. Programs also seem to produce more PhDs with practical skills now. I also note that many of the very top professionals—CEOs and CTOs and Tech Fellows—do have PhDs now. I do think it is hard to go back to school later. If you are only two years away from finishing, I would suggest you continue (unless it is really awful financially or otherwise.) It may not seem like it now, but when you’re 60, looking back over your career, two years will seem like nothing, whereas you may feel regret for not getting your degree. The book “Range: How Generalists Thrive in a World Full of Specialists” is the best book I know for thinking about your entire career path. Even if you are a specialist, “Range” talks about how you will go through many jobs and have to learn many skills in your life, and that these “big” decisions early in career are actually not as irrecoverable as they seem at the time. I wish I had read it when I was 29. I would have been more patient in some choices but also taken more risk

I did my PhD in SE after a couple of decades of working in industry. In my experience you can do a PhD in your 40s if you are motivated and have a worthwhile problem to research and solve. But you probably won't get much support. Academia is disinterested in solving real problems experienced in industry.

As you have discovered academia is driven by published papers, grants and the elusive concept of "prestige". Yes, AI is one of the more trendy areas for research at the moment.

The reality is that having a PhD does make you over-qualified for many jobs. Earning more? Only if you live and work in a city where there are lots of startups and/or firms that seek out people with above average knowledge and expertise in specific areas. For example, some PhDs ended up working as quants in the finance industry. Bio-tech is another possible area.

As for mental health, depression, imposters syndrome, etc are common. Less than half of PhD candidates complete their work and graduate. Sites like https://phdcomics.com reveal how widespread the challenges are. As for financial challenges, that too comes with the territory.

Don't take advice from people who don't have a PhD or don't hire those with PhDs. Reach out to alumni from your university and ask about their post academia experiences. In my case, I did the PhD for my personal interests. I had always wanted to do a PhD, but until I was established and able to self-fund, it was too risky. Clients hire me to solve their problems not because I have a PhD but because I have a track record in making money for my current and past clients.

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