[micro_cam]

I've been in the ML/data space for 20 years and went through the 2008 cycle early in my career. I'm not hiring at the moment but am always happy to review resumes and give career advice as part of networking so feel free to ping me.

Reaching out to managers at big cos won't get you much. They are getting a ton of twitter/etc candidates and have stricter hiring policies and rubrics to prevent nepotism and favoritism. If you can network and ask for referrals through your friends / professors that can work better. Or reaching out to early stage startup cofounders can work really well.

ML/AI is less frozen then some other areas so that is good. Most really competitive new PHDs will have either a couple of internships, strong academic contributions or previous engineering experience demonstrating strong coding ability.

You should also consider post docs or academic engineer postings. The grant cycle insulates these a bit from the economic cycle and they can be a good place to gather some experience while you ride out the cycle.

And definitely consider very early stage start ups. A startup that just raised and has 2 years of runway is probably one of the safest places to be at the moment as they are still focused on growth. A lot of great companies proved themselves as startups during the 2008 cycle and grew rapidly after. Networking can mean a lot more here as early stage founders often literally just hire their friends or people they get along with without a ton of process.

[rumdonut]

Somewhat unrelated but a remark of yours was interesting to me. You mentioned PhDs sometimes have previous engineering experience; did you find it common for ML PhDs to have waited a few years before entering a program? I’m exploring doing the same but had thought the ship had sailed, now that I’m in industry.

[micro_cam]

Not super common but its reasonably frequent for someone to go to industry for 2-3 years and then go for a masters/phd.

Those candidates tend to be great as they are up on industry practices/technology like source control and databases where as some phds can have only academic coding experience. And they tend to have studied something they really knew they were interested in and really been the driver on their thesis project vs just contributing to their advisors research.

You also see great phds who didn't wait but really took ownership of their project, used source control, figured out distributed computing, contributed to open source, scraped/built their own datasets, understood the real world implications and hacked on side projects to develop coding skills.

And you see some who just completed a theoretical + computational project their advisor suggested on an existing dataset with the minimal amount of coding needed and little thought to implications/applications.

[rumblefrog]

Thanks for the early start ups insight, also reached out on Linkedin :)