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Comment by angarg12 | Hacker News Reader

I moved to a Machine Learning Engineer role a few years back. I just did an internal transfer to a ML team with no previous knowledge. The team trusted me to pick up whatever I needed on the job.

Now I interview tons of people for MLE roles. Trying to get in "through the front door" is incredibly competitive. Not only you have to prove you are a great engineer, but also good ML knowledge. You'd be competing with people with MSc and PhD.

Whenever possible I still recommend following the path I did: become and excellent engineer, and then look for an internal team to transfer to. Any sensible manager will take you in and help you to grow into the role.

As always YMMV. This is my sample size of 1.

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Personally the path of becoming an excellent engineer is the best path (possibly hardest) to take on any situation, precisely because of the versatility in scenarios like this. I'm by no means an excellent engineer, but I like to plan and approach opportunities thinking someday I'll be one.

Starting the path is also age restricted. I've had some friends ask how they can switch into the industry by asking what languages they should learn. It's a hard pill for them to swallow, that they're better off becoming good at programming and great at IT, rather than trying to start from scratch after 30.

Your last sentence is confusing, rather than starting what from scratch after age 30? An ML career or trying to become a great engineer/programmer/IT person?

Great engineer like the poster above mentioned. Switching from web apps to ML isn't that big of a jump.

I moved to my current team in my mid 30s, so there is always hope :)

> Now I interview tons of people for MLE roles.

I come from fintech, what suggestions do you give to a former dev re-entering tech via AI and ML and wants to focus on the Product Dev side of things?

I managed/collaborated with a team of 3-4 devs as a co-founder during it's peak and then did a dev and consultant stint at a mega corp after getting fed up with how bad PM can ruin everything and self-sabotaging itself.

I'm now studying a BSc in AI and ML to get back into tech and I've realized that my strengths won't be as a developer and would I'd prefer to focus on being a much better PM than what I had.

I'm afraid my expertise is interviewing devs so I can't help you there. I'd also need more details to give more tips.

From the sound of it I'm not sure a BSc in ML will land you a PM job. I can also say that we are an applied science group. Our PMs are experts in our specific domain, not necessarily in ML.

As a Machine Learning Engineer what tools do you use on a regular basis?

Spark in Scala, Pytorch, Docker, bunch of infra... We run in AWS and have a ton of proprietary tools.

thank you.

> become and excellent engineer

Is there a reliable definition to that? A good balance between technical and communication skills or something among the lines?

> Is there a reliable definition to that? Not really, much of it depends on the group culture.

However in general this should be enough: Demonstrate you can do the work by "being smart and getting things done" and by not being a sociopath. If you are an asset and not a liability, you will become an excellent engineer.

Are these academic types great engineers? Do you find that the academics have a major edge in domain knowledge over self taught programmer?

You're not really an academic unless you're working in the academy, right? Once you move to industry, you're a professional.

Be consistent, a move to industry makes you industrial. It takes mere oath to profess.

Usually you can tell people who are purely academic. They excel at domain knowledge but are almost naive about building real world systems. They might come in as juniors. I don't think we would loop anyone as mid level or above unless they have experience delivering working systems in production.

Personally I don't mind the candidate's background so far as they can do the job (how the interview is a sucky way to determine this is another topic of conversation).

At the end of the day, it is about engineering. I'm self taught as a programmer, and I have also translated the latest deep learning paperwork into live code, AND i have novel research and experiments in code repos (not papers).

The thing lacking is a hacker's credibility as an ML researcher, which i think is ironically preposterous given the self-teaching of complex domains with engineered proof.

I don't see what an PhD has on the class of self taught hackers is what I'm trying to say. Just kidding, I know the answer is that they are trained in a certain institutional mentality. Give me my downvotes.

I lead a Ml Product team and in my experience no they’re not. The academic types are very proficient at research and exploratory data analysis. They’ll whip up a Jupyter in no time but they struggle understanding a live production system.

This is where an ML engineer comes into play, they’re not so academic but way better at writing production code

What of the engineer, who mastered a more complex domain on his own, perhaps innovates in that same domain regularly, do you suppose he can do the academic research?

Spicy comment >_>

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