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Comment by agentofoblivion | Hacker News Reader

I don't know anything about this company. But I work at FAANG as an ML scientist and spent about 3 months this year working on a research project that demonstrated big performance improvements of a new tech. It got enough interest that it led to a new project that brings this to production.

This has led to countless headaches. The skills needed to solve these challenges are very different. My more academically minded colleagues don't seem to have the intuition for seeing around this corner and understanding what it takes to build something that actually works.

This is a long way of saying: if a company is founded by and ran by such academics, I can see how this would be a recipe for disaster when it came to actually building and shipping products.

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Really good insight. When I put engineering job posts up, we get loads of PhD applications...a staggering amount. And all of these people look great on paper. It’s only when you start talking to them off script that you understand a career in academia has radically different incentive pressures than the private sector. We end up passing on most, not because they aren’t brilliant, but because they’re very one dimensional, which is what suits academia.

It must be surprising to find "brilliant" people who are apparently incapable of learning how to do new things.

I don't think it's fair to characterize such people as "incapable of learning how to do new things" if they're not even being given the opportunity to learn.

I'm not saying that the OP should hire these people, or that they would even be good hires. Maybe they don't want to take the risk or time of training up a new employee.

It just seems like a bit wierd non-sequitur to draw conclusions on someone's learning capabilities based on their performance in an interview outside their usual domain.

I think that's the point of the comment you reply to.

I don't find it surprising at all, sadly. After a reorg, one of my colleagues on a different team (largely Master's degrees in ML) told our new team member: 'You shouldn't be worried, being assigned to their team is an honor. They're hardest to get into -- it's full of people who didn't just skip all the hard courses.'

In many ways, hiring advanced degree holders is a crapshoot. They have skills you probably can't train, but often times come in with fewer software development skills than your undergrad intern, despite theoretically having more years of experience. You don't need to know git to publish in IEEE, or write unit tests or readable code, or debug an edge case, and your only code reviewer is a professor who doesn't care about this either. 'Good enough to publish' is a far cry from 'customers will pay for it.'

I never said they were incapable of learning new things. But they're usually not applying for junior level roles where we expect to make these investments. They are typically applying for senior roles where the applicant is expected to possess these soft skills.

In your opinion, what are the important 'soft skills' that

1) Academics lack

2) Are hard to pick up

E.g. another commenter mentioned Git. Many academics don't use version control, but I think learning how to do this to an acceptable level is not very difficult, so it doesn't really count.

I'm asking because I might be trying to get a job in industry soon.

I once worked at a consultancy that did government contracting. They had a hard time finding new software engineers to work as consultants.

So they got the idea, that they could hire other STEM Master's and PhD's, offer them an average software engineer salary and train them up to become good software engineers. Many software engineers scoffed at the idea - so you're hiring people from Physics to do web apps - pfff, what do they know about creating software?

Well, I'd say a third of my coworkers at the time were from "academia" - I felt absolutely no difference. The company is thriving and they have more than quadrupled in size since I worked there five years ago.

The skills needed to solve these challenges are very different

Indeed. I founded a contactless personalized food production and automation robotics venture in 2016. We tore through specialist and generalist employees alike trying to make progress toward a final prototype. We're now essentially there, but lost the team over COVID, while the investment environment for our technology is currently extremely hot. In taking stock of the situation to move forward, I've realised it's actually a blessing: the skills needed to reach mass-production on a very complex assembly are very different to the skills needed to iterate early and mid-stage prototypes rapidly and effectively. It would have literally been harder - and more expensive - to drag people 'over the line' in to production than to just hire a new team.

TransMeta.

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