zintinio5

The job description is to-the-point and doesn't ask for anything out of the ordinary. Other than mentioning a CS degree, I give it two-thumbs up.

Hiring is definitely a tough nut to crack. Personal referrals are by far the best source to get solid people quickly, but it's also a finite pool. Recruiters are definitely a mixed bag. I would suggest also sending a representative to a job fair (face-to-face still works!). It sounds like you're doing the best you can besides creating some really unique opportunity.

We had some luck bringing in people interested in functional programming, but ended up having to go heavy on junior devs since that's what's easy to hire for. We had some mid-level and a couple senior people which were hard to come by.

It's going to be a grind no matter how you approach it. All I can suggest is to make a really compelling offer, technologically, financially, or otherwise. Also consider finding older developers interested in part-time or remote work.

First, what does your careers page look like? Sometimes the job description is unclear or you might be filtering out qualified people. Second, mid-senior is really tough to hire for, I just experienced it first-hand while hiring for my last employer. Regardless of experience level, you are forced to compete on two fronts: interesting work, or high compensation. Mid-senior devs can afford to be very picky and if something seems a little bit off, they'll look elsewhere.

Have you partnered yet with a recruiting company? I personally just went through TripleByte and they could use a few more NYC partners. They do their own pre-selection of candidates to save everyone involved a lot of time. Another good one is FunctionalWorks. Meetups are also pretty good for making organic connections with higher-quality devs. Every time I go to a tech meetup at least 3 or 4 employers are represented and hiring.

That's not the case everywhere. Some companies pay managers less than engineers. From the company's perspective, a manager deals with a larger chunk of the business, while an engineer solves just one small part. Not saying it's right, but that's my simple guess.

We're pretty comfy here 9/12 months a year. The Spring and Fall are fantastic, and Winter is getting milder each year.

I would quit this team.

Coming from an under-represented demographic in technology, Hackernews has really been my insight into the industry and helped me immensely with figuring out how to get into it. Reading this website has honestly made me a ton of money and steered my career. On the job, I think Hackernews also keeps me sane by stimulating continuous learning each and every day. I really like this little community.

Thanks! I was browsing a few days ago and applied to work on Telemetry in Firefox and ML Engineering at Pocket. A lot of the work being done with respect to the Quantam initiatives seems interesting, but I didn't really see anything related to the core browser. Anyway, thanks a lot for the info.

How does one end up in a position to do this work at Mozilla? I'm currently looking for a new SWE role, would absolutely love to work on Firefox.

Still the same situation, even for CS majors from less known schools. Somebody has to take a chance, few people want to take a risk on you.

I will keep this in mind when researching jobs.

I've seen at least one or two companies working on optimization at least here on HN. I'd take a guess that Gurobi is one option, and I saw SigOpt post a listing on the recent "Who's Hiring?" thread. Google seems to also do a lot of optimization related work as well. I'm in the same boat, though I was stronger in CS during my studies. The background is useful, but you will only ever use bits and pieces of it at opportune moments.

Any chance you know someone hiring for said scientific computing roles? I studied Applied Math / CS undergrad, have been gearing up to work in Autonomous Vehicles, but really any challenging numerical work would be interesting to me.

Like everything else, depends on your use-case. I have personally used TF-IDF vectors and token sets with Cosine and Jaccard distances in practice.

Some examples of use-cases: are you searching for "semantically similar", or "near duplicate"? You can compare documents under different metrics and different _representations_. Some representations are: LSA, PLSA, LDA, TF-IDF, and Set representations, along with metrics such as Jaccard Distance, Cosine Distance, Euclidean distance, etc.

Doc2vec is the Word2vec analog for documents.

This is really awesome, thanks for posting! I was not yet aware that random forests could be used as kernels, and the link to Davies and Ghahramani is especially useful, definitely will read through later.