Building the Future of Audio AI, One Research Loop at a Time
There’s a certain kind of person who never really stops exploring.
The kind who finishes one degree… and still feels like there’s another layer to uncover. Another question to chase. Another version of the work they want to do. For Wentao Ma, that exploration took him from computer science studies in China, to Imperial College London and eventually to the MScAC program. Not because he lacked experience, but because he wanted a different kind of learning experience altogether.
And that distinction matters.
Already Experienced — But Still Looking for More
Before joining MScAC, Wentao had already built what many would consider a strong technical foundation. He completed a computer science degree at Beihang University, earned a master’s degree in computing at Imperial College London, worked as a software engineering intern at TikTok and spent time as a machine learning engineer intern at Sony.
On paper, it sounds like someone who already had things figured out. But for him, the bigger question wasn’t whether he could work in AI, it was how he wanted to work in it.
“I’d already experienced research, internships and coursework separately,” he explains. “What interested me about MScAC was the idea of combining all of them together.”
The 8-month coursework plus 8-month internship structure stood out immediately as a way to connect theory and industry in a much more practical, continuous way.
From Unclear Career Goals to Finding the “Middle Ground”
Before the program, Wentao describes his career direction as constantly shifting: industry? academia? engineering? PhD?
“I was moving backward and forward a lot,” he says. “The goal wasn’t very clear.”
That uncertainty started to change during his internship at Boson AI, where he worked as a Research Engineer focused on audio AI and large language models. And somewhere between reading research papers, training models, debugging experiments late at night and shipping features to customers, things began to click.
Not because he suddenly chose between academia and industry, but because he realized he didn’t have to.
“Working as a research engineer felt like the right balance between the two,” he explains. “I still get to do end-to-end research but also apply those results to real users.”
That realization gave shape to something that had previously felt abstract: a career path that blended research thinking with practical impact.
Inside an AI Startup: Flexible, Fast and Intense
At Boson AI, no two days looked exactly the same. The workflow itself felt research-driven: reading papers, reviewing technical reports, designing experiments, training models, evaluating results and continuously iterating to improve outcomes.
But unlike academia, there was also urgency. Customer impact. Product timelines. Real deployment. That fast-moving environment became one of the most rewarding parts of the experience.
The Network Effect
Ask Wentao what surprised him most about Toronto’s tech ecosystem: the number of MScAC alumni already leading teams, building startups and shaping the AI space across Toronto and beyond.
“I realized many of the really strong tech leads I met were MScAC alumni,” he says.
That network made the city feel more connected, more accessible and more collaborative. Especially for students entering competitive AI roles. And increasingly, those opportunities extend far beyond Canada. Wentao points out the growing connection between Toronto’s AI ecosystem and U.S.-based companies hiring talent directly from programs like MScAC.
For students thinking globally, that pipeline matters.
His Advice: Start Earlier Than You Think You Need To
Wentao’s advice to future students is surprisingly simple: start early. Not because you need to know everything immediately — but because momentum compounds over time.
Whether it’s learning new technologies, exploring AI research, building projects, or experimenting with tools like LLMs, he believes consistent curiosity matters more than waiting until you feel fully prepared.
He also encourages students to look beyond the “obvious” parts of the program. Yes, the courses matter.
Yes, the internship matters. But there’s also another layer: relationships with supervisors, side research projects, collaborations with professors and opportunities that often begin quietly before turning into something much bigger.
“The program gives students more opportunities than they expect,” he says. “You just have to actively develop them.”
And maybe that’s the clearest thread through his entire journey, not simply following a predefined path, but continuously building one.
