[citation_please]

I'm on a hiring committee for a job with the title of "Machine Learning Engineer". The most qualified candidates we have come through the process are physics PhDs who did a significant amount of coding during their PhD and have been able to segue into machine learning because they were interested.

Our other tier of hireable candidates have been individuals with 4+ years of industry experience, usually with CS PhDs with a machine learning specialty.

The rest of our team came from internal transfers, people who were less qualifed but proved that they would have a positive impact on the output of the team.

[drawnwren]

I'm curious why you are seeing such strong candidates from the physics department. Care to elaborate?

[analog31]

I'm a physicist, and got my degree in the early 90s, but maybe I can shed some light on this.

First, physics has been computation driven since the 1940s. When I was in grad school, programming was vital to my project. I wrote mountains of code, and also designed my own electronics.

Second, I noticed a weird difference between physics and engineering. Engineering students were given problems that were expected to be solved within a particular domain of engineering. A physics student might be given a problem with no idea of how to solve it, much less even how to define the problem itself clearly. That was my project. So a physics student could find themselves having to learn practically any technical skill.

Third, a matter of motivation. We knew that we would need to make ourselves employable. My project would occasionally fail in some spectacular way that would require me to learn more programming, or more electronics. Imagine that. ;-)

Fourth, possibly also as a matter of employability, math and physics people have always figured out how to worm our way into ill-defined, nascent areas of technology. These are areas that have not yet created a mainstream training pipeline, so we can plausibly claim to be as well trained as anybody. "Embedded systems" was such a field when I was finishing school.

[citation_please]

Adding onto this, every physics PhD I've talked to has really impressed me with how much problem-solving they tackled during their stint in academia. You really said it succinctly with "a physics student could find themselves having to learn practically any technical skill".

Running and extending numerical simulations with supercomputer clusters is just something you have to learn to do in order to solve your problem in some cases, apparently.

[citation_please]

Their ability to code is usually sub-par, but they ask the right questions about the data, which is critical in the data setting that we work in.

We don't work in advertising / marketing / business analytics, which is very easy to have an intuition about; and we don't strictly work with images, which, again, isn't terribly difficult to have an intuition about. So a strong scientific background is actually a major plus for us.

I could see that if we were doing purely deep learning image classification or advertising prediction then the physics degrees would be less useful, but thankfully we don't.

[ccdsacc]

Sounds like you are hiring for a really hardcore research role, if the most fitting candidates are CS PhDs with 4+ years of experience.

How would a person with a MSc in CS and 6+ years of work experience rank for you? (Converting ~4 years of PhD to 2 year MSc + ~2 year work exp)

[citation_please]

Probably the wrong fit if the industry experience isn't right, in which case they would be on a fast-track to a senior dev role in the non machine-learning part of our company.

You're right about "research", although I'd hesitate to use the word hardcore hah. We get tons of candidates with ~2 years of slightly relevant work, but rarely do we get candidates with the exactly right relevant work. The "qualified" candidates I spoke of have ~4 years of slightly relevant work, but there are only a couple of them, and they're either out of our hiring budget or currently working for our team.