Alex Yun graduated from the University of Toronto in 2015 as a Psychology Specialist with a Human Biology Major and Physiology Minor. He then pursued a master’s degree in Computer Science at the University of Waterloo with a focus on AI and affective computing. Given his unique interdisciplinary background, he studied the personality traits of individuals in online collaborative environments using computational methods.
He resides in Toronto and works as a Senior MLOps Engineer at Badal (acquired by Telus). He is passionate about using machine learning and deep learning to solve real-world problems.
Outside of work, he is an avid photographer documenting everyday life with his film cameras.
Tell us a bit about your time as a student at U of T. Looking back, do you have a favourite memory?
Like most students, I was mostly focused on getting good grades and trying not to sleep in for morning classes (or avoid them altogether during course enrollment).
I was relatively involved with the psychology community—namely, the Psychology Students' Association (PSA). Some of my favourite memories include organizing and attending PSA events, where I met some of my closest friends. As absurd as it sounds, I miss the late-night study sessions with friends and getting food delivered to Sidney Smith.
What did you find particularly challenging about being a student? How did you work through it?
One of the challenges I faced as a student was the lack of structure. Sure, lectures happened at scheduled times and assignments had clear deadlines, but keeping myself on track required a lot of self-discipline. I found it especially difficult to context switch—knowing when to stop and start a new task. Whether it was studying for another subject, remembering to eat, or even going to bed, I struggled to pull myself away from whatever I was doing.
My solution was to prioritize tasks, break them into smaller chunks, and set alarms to remind myself when to stop—or start—something new. Funnily enough, I still use an alarm that reminds me to grab my last cup of coffee for the day (at a reasonable hour that won't mess with my sleep).
If you could go back to your first day at U of T, what advice would you give yourself?
If I could give my past self some academic advice, I'd actually suggest studying less—with the caveat to study more efficiently. Many students fall into the trap of thinking that long hours in the library automatically translate to learning. I often tell students to try the Pomodoro technique: structure study hours into manageable sessions, log those hours, and use those data points to optimize learning.
Beyond academics, I'd also encourage myself to stay open and say yes to as many experiences as possible. Study abroad for a term, travel with friends, do an internship, join an interest-based club, meet new people—explore widely. Those experiences matter just as much, if not more, than what happens in the lecture hall.
What is one thing you learned in your psychology classes that you still think about today?
Because I was interested in cognitive neuroscience, I still think often about the Bayesian brain hypothesis, which postulates the brain as an inference machine that makes predictions based on past experiences. I find it especially helpful to keep this concept in mind when engaging with people who hold opposing views. It reminds me that people don’t just hold different beliefs; they experience the world differently. Knowing this allows me to approach others with more understanding and compassion.
During my time as a student, I also discovered mindfulness, which has since become a lasting part of my life. Engaging with my thoughts in a quiet, intentional way has helped me find calm and clarity, both then and now.
What does a typical day look like for you at work? How do you use what you learned as a psychology student?
Technology-driven organizations often follow agile practices, which break projects into different phases. My typical day can vary significantly depending on which phase we are in. Generally, I reserve mornings for deep work—problem-solving, programming, and other focus-intensive tasks—while afternoons are set aside for administrative work.
Meetings are spread throughout the day and include daily standups, project scoping, collaboration with coworkers, and reporting to stakeholders. Contrary to popular belief, actual coding occupies a relatively small portion of my time.
As a former personality researcher and someone who's taken courses in organizational behaviour and individual differences, I occasionally think about how to optimize collaboration and interpersonal dynamics in the workplace.
Which experiences from your undergrad have you found to be most useful in your career?
Looking back, some of the most useful experiences from my undergrad weren’t necessarily tied to specific content, but to how I learned. Taking seminar courses, for example, taught me the value of active engagement—being able to shape discussions collaboratively with my peers. That kind of dialogue and critical thinking is something I now engage in daily at work.
Being part of a research lab was another foundational experience. Having the opportunity to run a project from start to finish helped me develop not only technical skills, but also project management, problem-solving, and perseverance—skills that continue to serve me well in a fast-paced, ever-evolving industry.
Finally, leading the undergraduate psychology journal Inkblot gave me valuable experience in team coordination and communication. Working closely with editors to plan, delegate, and execute ideas taught me how to balance detail-oriented work with broader collaboration—all of which are deeply relevant to what I do now.
How would someone who is interested in the kind of work you do get started on a similar career path?
If your goal is to work in tech, it is important to assess your risk tolerance as the industry can be quite volatile. Are you comfortable knowing that layoffs are more common here than in many other fields? What about the reality that skills you have spent years developing can become outdated almost overnight due to shifting trends and rapid advancements in AI? Many of us in the industry have to continuously learn and reinvent ourselves to stay relevant.
If you are specifically interested in a career in AI or ML, you will need to build a strong foundation in both math and technical skills. At a minimum, this includes studying subjects like linear algebra, calculus, probability and statistics (especially Bayesian), programming, data structures, and algorithms. While a degree won’t necessarily prepare you perfectly or guarantee you a job, many AI-related roles require a specific educational background.
That said, I have also seen many of my psychology peers thrive in tech by pursuing paths in software engineering, UI/UX design, or project and product management. There’s no single path into the industry, and your unique background can absolutely be a strength.
What advice would you give soon-to-be grads that are thinking about what comes next?
If you are reading this as a soon-to-be grad, I want to assure you: it's okay not to have everything figured out. I took nearly a three-year gap between undergrad and graduate school—an invaluable time where I reconnected with my family, myself, and experienced significant personal growth.
Many things in life, including your career, will not go according to plan and that's completely normal. Be curious and trust your intuition. Step outside of your comfort zone, try new things, challenge yourself, allow yourself to fail, and keep going.
This quote deeply resonated with me and helped me slow down during a time when life felt like it was moving too fast:
“The privilege of a lifetime is to become who you truly are.”
—Carl Jung