Shifting Careers to Autonomous Vehicles

Props to the author--landing your first deep learning position is very difficult. There are many many engineers who want to do deep learning but don't know how, and a few employers that badly need deep learning experts. The employers are (understandably) skittish about taking a chance on hiring someone without expertise and assuming they can train up. If you can show up with proof that you can train performant models, then you'll have a much easier time. Certainly the author put in his time to build up this proof.

By-the-by, if you want to work on autonomous vehicles and aren't too attached to being neck deep in the deep learning parts, nearly all autonomous vehicles companies need an army of engineers for data handling, and on-vehicle performance.

(Vladimir, if you're reading this, I'd be curious to hear what your $$$ investment was for your kaggle competitions).

The amusing thing is that people assume autonomous vehicles are a deep learning problem. So far, the pure deep learning people haven't done all that well at it. Most of what Waymo does is geometry and sensor fusion. Visual object recognition and future behavior of moving obstacles is used, too, but that's not the core of the system. Look at their videos, showing an above view of what's around the vehicle and "fences" showing where it shouldn't go.

I always find it funny when I see some British recruiters' posts in my LinkedIn network regarding "awesome Deep Learning opportunities" who require a PhD from a top school, publications at top journals/conferences and offer 65k GBP. Or some "super hot" Deep Learning startups seeking engineers for $3500-5500/month before taxes. For some, having Stanford Deep Learning on resume means one would satisfy minimal requirements for fixing bugs in their crappy robotics system (and they are nothing like Tesla).

I love posts like these. The author really has awesome persistence and great sense of initiative. Its really good that Kaggle now has GPU training otherwise AWS or GCP will just burn a big hole in your pocket. Handson experience with this some time back.

The credentialism in deep learning is absurd. This guy has a PhD in theoretical physics from UC Davis, he's winning competitions on Kaggle, and he can't even get an interview.

I remember that moment to this day. I am standing at the scene. The organizer is preparing a check and some gifts. Alexey and I emerge victoriously, but I can’t help but feel frustrated by the apparent absurdity. How did this happen that some random dude that does not have domain knowledge in medical imaging got first places in both challenges? And yet, people that make money for a living working on medical imaging use much weaker models?

I asked the audience: “Do you know where do I work?” Noone, except one organizer who checked my LinkedIn profile knew. I told them that I work at TrueAccord, a debt collection agency and that I did not train deep learning models at work. I lamented that I was not able to break away from this model because HRs in Google Brain and Deepmind did not even look at my resume.

Kudos to your effort and resilience even in the face of failures. You faced your challenges head on and took the bull by the horns! Love it!

Very impressed how the author was able to bootstrap your way into a good job.

This is inspiring! Excellent work and write up. Thanks for sharing.

This is an inspiring story. Nice work and well-earned!