How I Landed My Dream Job Working On Self-driving Cars
I graduated high school in 2009 and I knew back then that the Tesla Roadster was cool — really cool. I paid attention to Tesla Motors and followed the release of the Model S. During college I would go to the nearest Telsa showroom and go for test rides just for fun. Now, I had no idea what the future held. I was studying mathematics and art at the time. I got into deep learning (DL) through style transfer and was hooked. It was only after I graduated that I began to learn about all of the applications DL was about to revolutionize — especially autonomous vehicles.
First Things First
Like a lot of young people, I hadn’t the foggiest idea what I was going to do when I graduated last fall. Mathematics is a wonderful thing, but it’s not very career specific. Just a few months after graduating, I made two very important decisions: to enroll at Metis and to enroll in the Udacity Self-driving Car Engineer Nanodegree (SDCEND). Both of these were instrumental in my career path, but the Udacity SDCEND was critical.
When I first signed up, I wasn’t expecting to end up working in the self-driving car industry. All I knew was that I was genuinely interested in the content being taught and that it would be applicable in multiple domains. Later on, Udacity released Nanodegree programs in Robotics and Artificial Intelligence. There is a lot of overlap in all three of these programs. Eventually, I’d like to take all three of them.
The Self-Driving Car Nanodegree program
The Udacity SDCEND consists of 3 terms, each term lasting 12 weeks. You can read about the course syllabus for each term here. The entire Nanodegree program touches on all aspects of the autonomous vehicle pipeline by having students build out multiple projects per term. The goal is to help students be well-rounded and understand the fundamentals of the big picture, as well as develop an impressive GitHub presence (and a blog too, if you do the career support extracurriculars!) For me, it means being exposed to a lot of engineering (which is new to me) and tying it into machine learning (my passion).
Udacity partnered with several corporate sponsors for both content creation and as hiring partners — names like Mercedes-Benz, BMW, and NVIDIA. Currently, I’m watching videos made by engineers at Mercedes-Benz and I’ve applied to McLaren Applied Technologies through the private hiring portal. To me, this is amazing and it shows how seriously Udacity is taking the emergence of the automation revolution. Before I was a student, I didn’t take Udacity’s open-source self-driving car very seriously. Now, I want to become a contributor (more on this soon!) You can check out their GitHub repository for their self-driving car here.
I’m not the only student who has become more committed to the field through the program. The private Slack channel for students is filled with a tangible excitement. I’ve never been a part of a such a large student body, let alone a student body that is committed to the success of every student (no grading curve here). Between Slack, the dedicated forums, and your own private mentor, there is no reason to be stuck on a problem — there are so many people willing to help answer your questions. Instead, you can focus on finding your own way to improve the foundations of the projects.
What to Expect
The course took me about 10 hours per week. This can vary a lot depending on your level of experience with coding, machine learning, engineering etc. If you’re anything like me, this means zero hours per week until the project is close to due, and then working feverishly all weekend. I managed to complete Term 1 while attending an immersive data science bootcamp and I’m doing Term 2 while working full-time.
*warning — I’m about to get on a soapbox* If all you want is to pass the program and add a certification to your LinkedIn, the program expects very little of you. As the program grows, there are more examples available, and more answers documented. If you want to work on self-driving cars, you need to take ownership of the curriculum. You need to do research outside of the lectures. You need to incorporate the examples and answers that are available and then build on them. The program is not designed to make you a principal engineer. It’s designed to help you get your foot in the door — to give you a platform to demonstrate your work ethic and passion. This is exactly what got me my job.
I finished Term 1 of the Udacity SDCEND about the same time I graduated from Metis. I spent several weeks utilizing every job search trick in the book and sent out close to 50 applications for various data science positions. I interviewed with several companies leading up to graduation and in the weeks that followed.
Ultimately, I received an email from a principal engineer at HERE Technologies asking if I would like to interview with him about an internship opportunity on his Highly Automated Driving team.
I chose HERE for a few reasons:
- The principal research engineer who made me the offer is an outstanding person. I was more than happy to work under him.
- The work and commitment I demonstrated by transitioning from pure mathematics to self-driving car technology was recognized and rewarded.
- I am working with experts in computer vision and deep learning. I knew this position would set me up to continue learning.
- The job was in Boulder, CO. I was happy for the change of pace and to hit the slopes!
- I’m part of the Highly Automated Driving team. Our platform and models are going to impact the world in a very real way, very soon.
HERE is primarily a mapping company (HERE WeGo). They have an established track record of success since the 80’s and have been working in the automotive space since 2015 when they were purchased by Volkswagen, BMW, and Daimler — later Tencent and Intel would also purchase a stake in the company.
There Is No Spoon
Technology is an amazing industry to work in. Often it’s the case that merit and ability are rewarded as much or more than seniority. If you have access to the internet, you can become proficient at a seemingly endless number of tech-related skills. If there is something you want to work on, you don’t need to wait to get started. Dive in, build a portfolio, and demonstrate interest.
If you complete a program like the Udacity SDCEND, be open when opportunity knocks. I accepted the internship with HERE despite the fact that I am doing DevOps work instead of building sexy convolutional neural networks. The self-driving car pipeline is massive. There are so many different specialties coming together to build something remarkable. Whether it’s what you work on, or what kind of work you do, find out what’s important to you and pursue it.