Not unusual for people at the top of their game. I just turned down a 2M a year offer, and I have a friend who is making 2.9M USD. It's just a value proposition, he makes the company a lot more than 2.9 million a year, and when you negotiate you negotiate on value (and not on time) then you can justify these salaries.
What is considered to be "top of game" in deep learning in your experience... does that apply to just researchers like Ian Goodfellow who come up with completely novel methods for ML algorithms, or does it extend as far as people who are just using the methods that others developed effectively or in new ways? I know thats a weird question, but I am planning on looking into deep learning jobs after finishing my (MS) curious what the market value is for people who have experience implementing the systems , vs the people inventing new architectures. Because I won't have a PHD... It seems like somewhere along the way its a pretty extreme jump to ask for 1,2M or even 500K instead of just 100K~200K... wondering if you have any advice for how someone new might prove themselves... I guess beyond the standard stuff (have a nice github, try to replicate papers etc...)
I think a smart, hard working person who re-uses modern results from others well and in potentially new ways can create a vast amount of value. Short term, more than the top guys, as a lot of their work may be speculative, and yours would be getting-it-done. Long term, they'll invent some method that leaves you in the dust, but that's fine, just learn that too!
I respect fake-it-till-you-make to some degree, but I find too much "spinning" actually hurts the ML industry (though no doubt profitable for the spin artists). Communication style straight from the ICO world.
It's experience in the two examples I gave : I singlehandedly built my company : RAMM Science (https://ramm.science)
And he singlehandedly built one of Europes largest online betting system backends (and scaled it to 800 transactions a second using Deep Learning, Kafka and a Hadoop cluster)
Is 800 transactions per second good? It seems like a low number but what do I know. I am impressed that Europe's largest betting system only does 800 transactions per second.
That's 800 credit card transactions a second --- thats people making deposits into their accounts to bet with. (btw I think that's peak - I'm not 100% sure)
The frontends (where the casino / betting games run) do a lot more than this, but they work against the balance in the account, it's the payment gateway that's doing 800/s
It was a bit simplistic of me to summarize it as a payment gateway, it was actually 50-60% of their backend systems that was enhanced, it was called their "Big Data Project"
They do do fraud detection on the deposits, because of anti-money-laundering. The DL models also monitor various other types of abuse (bonus abuse and in-game abusers). They also are experimenting with Deep Reenforcement Learning to actually play some of the games
Well, I think random forests on a feature set probably plays a big role and is probably easier. But to use a deep neural network maybe, you might be able to get away with a convolutional network to extract higher order associations among some input set.
An online betting system has to do fraud detection on a higher level than random merchants, since they can expect to be a target for large scale non-amateur fraudsters, and just as in any other business, if your incoming payments are fraudulent, you lose that money.
Degrees don't matter. (Business focused) results and a track record of delivering do. Still impressive remuneration though -- he must be adding tens of millions in profits to the bottom line.
Degrees matter deeply among the untalented in my experience. I have one of those fancy pee HUDs, but I only whip it out when I'm dealing with the above sort.
Results don't really matter either, you just need to convince people that you are valuable. Many people in business don't have any sort of "provable" results but have excellent people skills.
