Xiaoyi Yin (尹肖贻) has kindly translated this post into Chinese (中文)
This post was prompted by a tweet I saw from my colleague, Colin:
I'm currently a researcher at Google with a "non-traditional background", where non-traditional background means "someone who doesn't have a PhD". People usually get PhDs so they can get hired for jobs that require that credential. In the case of AI/ML, this might be to become a professor at a university, or land a research scientist position at a place like Google, or sometimes even both.
At Google it's possible to become a researcher without having a PhD, although it's not very easy. There are a two main paths [1]:
One path is to join an AI Residency Program, which are fixed-term jobs from non-university institution (FAANG companies, AI2, etc.) that aim to jump-start a research career in ML/AI. However, these residencies are usually just 1 year long and are not long enough to really "prove yourself" as a researcher.
Another path is to start as a software engineer (SWE) in an ML-focused team and build your colleagues' trust in your research abilities. This was the route I took: I joined Google in 2016 as a software engineer in the Google Brain Robotics team. Even though I was a SWE by title, it made sense to focus on the "most important problem", which was to think really hard about why the robots weren't doing what we wanted and train deep neural nets in an attempt to fix those problems. One research project led to another, and now I just do research + publications all the time.
As the ML/AI publishing field has grown exponentially in the last few years, it has gotten harder to break into research (see Colin's tweet). Top PhD programs like BAIR usually require students to have a publication at a top conference like ICML, ICLR, NeurIPS before they even apply. I'm pretty sure I would not have been accepted to any PhD programs if I were graduating from college today, and would have probably ended up taking a job offer in quantitative finance instead.
The uphill climb gets even steeper for aspiring researchers with non-traditional backgrounds; they are competing with no shortage of qualified PhD students. As Colin alludes to, it is also getting harder for internationals to work at American technology companies and learn from American schools, thanks to our administration's moronic leadership.
The supply-demand curves for ML/AI labor are getting quite distorted. On one hand, we have a tremendous global influx of people wanting to solve hard engineering problems and contribute to scientific knowledge and share it openly with the world. On the other hand, there seems to be a shortage of formal training:
- A research mentor to learn the academic lingo and academic customs from, and more importantly, how to ask good questions and design experiments to answer them.
- Company environments where software engineers are encouraged to take bold risks and lead their own research (and not just support researchers with infra).
Free Office Hours
I can't do much for (2) at the moment, but I can definitely help with (1). To that end, I'm offering free ML research mentorship to aspiring researchers from non-traditional backgrounds via email and video conferencing.
I'm most familiar with applied machine learning, robotics, and generative modeling, so I'm most qualified to offer technical advice in these areas. I have a bunch of tangential interests like quantitative finance, graphics, and neuroscience. Regardless of technical topic, I can help with academic writing and de-risking ambitious projects and choosing what problems to work on. I also want to broaden my horizons and learn more from you.
If you're interested in using this resource, send me an email at <myfirstname><mylastname><2004><at><g****.com>. In your email, include:
- Your resume
- What you want to get out of advising
- A cool research idea you have in a couple sentences
Some more details on how these office hours will work:
- Book weekly or bi-weekly Google Meet [2] calls to check up on your work and ask questions, with 15 minute time slots scheduled via Google Calendar.
- The point of these office hours is not to answer "how do I get a job at Google Research", but to fulfill an advisor-like role in lieu of a PhD program. If you are farther along your research career we can discuss career paths and opportunities a little bit, but mostly I just want to help people with (1).
- I'm probably not going to write code or run experiments for you.
- I don't want to be that PI that slaps their name on all of their student's work - most advice I give will be given freely with no strings attached. If I make a significant contribution to your work or spend > O(10) hours working with you towards a publishable result, I may request being a co-author on a publication.
- I reserve the right to decline meetings if I feel that it is not a productive use of my time or if other priorities take hold.
- I cannot tell you about unpublished work that I'm working on at Google or any Google-confidential information.
- I'm not offering ML consultation for businesses, so your research work has to be unrelated to your job.
- To re-iterate point number 2 once more, I'm less interested in giving career advice and more interested in teaching you how to design experiments, how to cite and write papers, and communicating research effectively.
What do I get out of this? First, I get to expand my network. Second, I can only personally run so many experiments by myself so this would help me grow my own research career. Third, I think the supply of mentorship opportunities offered by academia is currently not scalable, and this is a bit of an experiment on my part to see if we can do better. I'd like to give aspiring researchers similar opportunities that I had 4 years ago that allowed me to break into the field.
Footnotes
[1] Chris Olah has a great essay on some additional options and pros and cons of non-traditional education.
[2] Zoom complies with Chinese censorship requests, so as a statement of protest I avoid using Zoom when possible.