Not Your Average Residency

An Interview with Jinnah Ali-Clarke, The First Graduate of’s AI Residency Program

Jinnah Ali-Clarke is the first graduate of’s AI Residency program. Alyssa Kuhnert, who leads Communications at, interviewed him to get a first-hand account of his experience so far and what he’s up to after graduating earlier this spring.

AK: Hi Jinnah, excited to talk with you today about your experiences as’s first AI Resident. To begin, how did you started in the field? How did you first find out about the opportunity here at the company?

JAC: Approaching the final year of my Bachelor in Mathematics at the University of Toronto, I wasn’t fully sure what I wanted to do after graduation. The idea was that I would go to med school because you want to do what your parents want you to do, right? I eventually decided that I didn’t want to spend 8 years doing something that I really wasn’t interested in, so I decided to enrol in a program for software development.

Finishing that led me to the opportunity at, which some friends from the Toronto tech industry had mentioned as a really awesome AI company. At that point, I didn’t have the skills required for a full-time gig here, but was lucky to have the opportunity to join the team as an AI Resident at and build my skills in both software and machine learning.

AK: So instead of becoming a medical resident, you ended up becoming a machine learning resident! Now that you’ve recently finished up the residency and are on full-time as our DevOps Engineer, can you share the key lessons you learned by taking part in the program?

JAC: During the eight months I took part in’s Residency program, I learned more than I ever did at school. School teaches you how to manage your time, working here has taught me a ton about how to solve much more specific and tangible problems.

When the Residency was coming to an end here, I still had to go through a pretty tough vetting process to land the current DevOps position I’m working in now. Getting a hands-on opportunity to develop my knowledge of the tools powering work during the Residency gave me the foundation I needed to get there.

AK: You mentioned your background in Math and Physics at UofT. Do you think it’s given you a distinct advantage learning the ropes of machine learning?

JAC: When it comes to specifics, it really hasn’t helped with much other than giving me a head start understanding concepts like gradient descent, things like that. One thing math has provided me with though is a really helpful set of problem solving skills. Studying math gives you chops when it comes to being able to have intuition about whether something will turn out — or not.

AK: When it comes to AI, what’s the most surprising thing you think you’ve learned since starting at

JAC: Because the field’s still so new — especially in terms of real-world applications, I was surprised to find out that machine learning right now is more alchemy than most people think. What I mean by that is that it’s less of a science than I first would have thought. Right now, a lot of it’s focussed on experiments: sometimes you just have to throw something at the wall and see what sticks.

AK: What do you like about working at

JAC: The best thing is the culture.This company isn’t like other companies. The atmosphere at other places is very rigid and boring, with a lot more set process. Here it is different, you can really talk to whoever you want, whenever you want. Everyone is always willing to help.

AK: I’d have to agree with that! As a team we’re always striving to collaborate, so I’m glad that’s a perspective that’s shared. Since finishing up your Residency here you have joined the team as a DevOps Engineer, contributing to the development of our AI platform. What has it been like so far helping build an AI platform for enterprise?

JAC: A lot of the platforms that exist for working on AI systems right now are conceptually cool, but aren’t as helpful as they could be — some of them have even turned out to interrupt machine learning engineers’ workflows. Collaborating with the machine learning engineers both within our team and through our work with enterprises will continue to be key throughout the platform’s development: with it, we want to build tools that will make people’s lives easier. Getting feedback from people who work hands-on in the field to find out what works and what doesn’t has been huge — we’re building things that help AI shift from hype to reality.

AK: Do you have any advice for anyone who wants to get into AI?

JAC: The best way to improve skills is to actually do something in that direction, so if you’re interested in machine learning, my advice would be to get started now! Practice makes perfect so start working on side projects or at least start reading more about machine learning. Machine learning also needs the contributions of many different kinds of people, especially software developers. Even though we’re now moving on to building machines that learn, someone needs to program those machines to accept the data, and that probably won’t be a machine learning engineer.

About’s AI Residency Program:’s AI Residency Program provides recent graduates with hands-on opportunities to evolve their skills in software and machine learning to build their capabilities in applied AI. Successful candidates engage in a software development training role at the company for eight months, with access to mentorships, on-the-job training, opportunities to work on special projects and more.’s AI Residencies are fully subsidized opportunities.

After completing a Residency, graduates can elect to complete a series of technical tests to be considered for full time openings at Successful employment following the Residency program is conditional and based on technical merit and the company’s current talent requirements.

Interested in applying? We’re looking for candidates with diverse backgrounds in STEM who want to deploy impactful end-to-end AI solutions that solve real-world business problems. Get in touch with us at

Source: Deep Learning on Medium