[dekhn]

I have a long history. I started out as a Biophysics PhD doing MD simulations and applying machine learning to biology (search for 'David Konerding' in Google Scholar). Since Google is very much like academia, my research training prepared me to be successful inside. Getting inside was the hardest part!

When I joined google, the only way I could get hired was as a test engineer on an SRE team, which I then converted into an SRE via mission control and wrote docs and shared them internally with folks until the principal engineers read them, and then they gave me infinite resources (Google Exacycle) to do everything I described above. I used that to get promoted to Staff SWE, and used that to launch a product (Google Cloud Genomics) and do some interesting machine learning for drug discovery (BTW, at this point mny career was effectively complete- I had set out to do everything I wanted, and was interested in what to do next).

The above happened because (beyond a wide range of boosts provided by parents and country) I have an intense drive, wanted to work at Google more than anything, and exploited the internal structure of the company to maximize my power. I kept networking to meet more and more people, and by meeting those people I got more access and support. I helped build up a team- Google Accelerated Sciences- which does basically what I thought Google should be doing all along.

unfortunately, at that point Google politics and personalities intervened and I was kicked out of the cool kid's club.

[Aperocky]

> I have an intense drive, wanted to work at Google more than anything, and exploited the internal structure of the company to maximize my power

> at that point Google politics and personalities intervened and I was kicked out of the cool kid's club.

It sounds like a power struggle gone the other way from reading this. And it certainly doesn't seem like either of the sides are more noble than the other.

But props for you for doing what you loved best, if only one day any of these can be decoupled from politics.

[alisonkisk]

There's a difference from politicking into a club and politicking others out of a club.

[rejectedandsad]

Wow, very impressive! Turns out I've followed your work for a while and never knew your HN username. Makes me wish I had the innate ability to be able to achieve even a fraction of this or have a scrap of the prestige - instead, I'm stuck on a lower rung for good (I blame heritability of intelligence via my parents).

[dekhn]

Huh, OK, I didn't know anybody followed my work! Note that I am not particularly intelligent- I always struggled in school, and had a ton of imposter syndrome. I tried and failed to become a successful scientist and instead pivoted to what I'm actually good at (scientific computing). Most of my drive came from fear of not being able to make enough money to live in the bay area, or from being thought unintelligent.

Never underestimate the power of imposter syndrome!

[selimthegrim]

Where would you go now if you wanted to do such things? I have people in UW CS tell me a physics PhD in CMT is no proof I can learn how to analyze RNASeq data quickly enough for them and they want a postdoc with the experience already to teach DL to (I would have thought the other way around would be easier)

[timy2shoes]

The other way around is definitely easier. RNA-seq analysis is a mostly solved problem with DESeq2 (or edgeR/limma). The tutorials are very detailed. The most difficult part is experimental design, which you probably know already.

Deep learning, on the other hand, is so fraught with pitfalls and traps. Even if you can code up a model successfully, it's very easy to trick yourself that you're doing very well (see a previous discussion at https://news.ycombinator.com/item?id=27376839). In my opinion, most of the work should be spent on making sure that you're not tricking yourself.

[dekhn]

literally the first 5 years of training in any large scale data analysis should be "how not to trick yourself into thinking you found something significant that generalizes"

[selimthegrim]

So then why I am getting static from these PIs? Do they have some R01 report deadline a year away breathing down their necks?

[dekhn]

There's a reason I invented this saying: "The very best machine learning models in biology are distilled from postdoc tears".

[gone35]

> I helped build up a team- Google Accelerated Sciences- which does basically what I thought Google should be doing all along.

MD?

[shadowfox]

Probably "Molecular Dynamics".

[dekhn]

Yes. also known as "the computational job which will consume all CPU forever, while not answering useful scientific questions".