Google is hiring and there are lots of opportunities to do Machine Learning-related work here. Kevin Murphy is applying Bayesian methods to video recommendation, Andrew Ng is working on a neural network that can run on millions of cores, and that's just the tip of the iceberg that I've discovered working here for last 3 months.
There is machine learning work in both "researcher" and "engineer" positions, and the focus on applied research makes the distinction somewhat blurry.
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What's the best way to get into ML research section if I am getting a call from the engineering HR (they found me so I was not choosing)?
If you want "researcher" position, you probably need to apply to it separately. However, Google is engineering oriented company, and there are very few "researcher" positions. Most Machine Learning PhDs around here are actually engineers.
Thanks for the reply. Here is a follow up question. Is there a way to keep publishing while being an ML engineer and how hard it is (publishing)?
At a research scientist position posting: http://www.google.com/#hl=en&q=google%20research%20scientist&fp=1&cad=b one of the requirements is: Maintain an active academic presence, ideally in collaboration with other Google researchers and engineers.
Is this encouraged/discouraged for an ML engineer?
It varies a lot by the team -- if you are in a team of engineers with academic backgrounds, then you publishing papers will be appreciated. For instance, my team has an annual goal of one paper per year. Then there are teams that are more interested in immediate real-world impact and won't understand why you want to spend time on a paper.
Initially they try to allocate you based on initial interests, ie, if you did object recognition, they'll send a request to some image recognition teams like Like.com and see if they have openings. Engineers have the ability to reallocate themselves, so if initial allocation sucks, it's not so bad in the long run. A friend of mine didn't like his team and switched to google books after 5 months.
A general rule of thumb for Google manager is to avoid saying "no." If you really want to do something, I think you'll be able to do it. I think the main obstacle to staying up on academic research comes from within -- once you have access to exacycles of computation and petabytes of data, your perspective changes
Very useful information. Thank you!
So Andrew Ng works at Google ?
a lot of options for machine learning seem to be for PhD grads, do grrads from MS in machine learning really get any opportunities?
Is there any opportunities for MS(by research) grads in Computer Vision/Machine Learning field for internship & employment in Google ?
Internship is only available to currently enrolled students
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