Three years ago, Cruise, an autonomous-vehicle startup acquired by General Motors, had about 50 employees. At the beginning of 2019, the head count at its San Francisco headquarters—mostly software engineers working on projects involving machine learning and artificial intelligence—hit around 1,000. Now that number is up to 1,500, and likely to reach about 2,000 by year-end, sprawling into a building that had housed Dropbox. And that’s not counting the 200 or so tech workers that Cruise is aiming to install in a Seattle satellite development center and a handful of others in Phoenix and Pasadena, Calif.
Cruise’s recent hires aren’t all engineers—it takes more than engineering talent to manage operations. And there are hundreds of so-called safety drivers that are required to sit in the 180 or so test vehicles whenever they roam San Francisco. But that’s still a lot of AI experts to be hiring in a time of AI engineer shortages.
Hussein Mehanna, head of artificial intelligence and machine learning at Cruise, says the company’s hiring is on track, due to the appeal of the challenge of autonomous vehicles. Mehanna himself joined Cruise in May 2019 from Google, where he was director of engineering at Google Cloud AI.
Mehanna has been immersed in AI and machine-learning research since his graduate studies in speech recognition and natural-language processing at the University of Cambridge, in the United Kingdom. I sat down with him to talk about his career, the challenges of recruiting AI experts, and autonomous-vehicle development in general. [Editor’s note: This interview has been condensed and edited for clarity.]
Tekla S. Perry: When you were at Cambridge, did you think AI was going to take off ?
Hussein Mehanna: No. I do recall in 2003 that my supervisor and I were wondering if neural networks could help at all in speech recognition. Now neural networks have dominated vision, speech, and language processing. But that boom started in 2012.
I didn’t expect it, but I certainly aimed for it when I was at Microsoft, where I deliberately pushed my career toward machine learning instead of big data, which was more popular at the time. And I aimed for it when I joined Facebook. In the early days, Facebook wasn’t that open to Ph.D.s, or researchers. It actually had a negative sentiment about researchers. And then Facebook shifted to becoming one of the key places Ph.D. students wanted to join.
T.P.: Is it getting harder or easier to find AI engineers to hire, given the reported shortages?
H.M.: There’s a mismatch between job openings and qualified engineers, though it is hard to quantify it.
Here at Cruise, demand for AI talent is just growing and growing. It might be saturating at other kinds of companies that are leveraging more traditional applications—ad prediction, recommendations.
The autonomous-vehicle problem is the engineering challenge of our generation. There’s a lot of code to write, and if we think we are going to hire armies of people to write it line by line, it’s not going to work.
Sometimes people worry that AI is taking jobs. It is taking some developer jobs, but it is generating other jobs as well, protecting developers from the mundane and helping them build software faster and faster.
T.P.: Where are you looking as you try to find a thousand or so engineers to hire this year?
H.M.: Because autonomous-vehicle technology is the new frontier for AI, the number of people with both AI and AV experience is quite limited. So we are acquiring AI experts wherever they are. You don’t have to be an AV expert to flourish in this world.
There are endless applications to be developed over the next few decades. Even if we can get a car to drive safely, there’s the question of, How can we tune the ride comfort and then apply it to different cities, different vehicles, different driving situations? I can see how I can spend a lifetime trying to solve this problem. —Tekla S. Perry
An extended version of this article appears on our View From the Valley blog.
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CORRECTION: Due to an editing error, the August Hands On article, “Making Machine Learning Arduino Compatible,” referenced “8-bit automatic-voice-recognition processors.” This should have read “8-bit AVR processors.”