$ cat jobs/phd-research-scientist-intern-canva-b7184f103ef5.json
PhD Research Scientist Intern
Join the team redefining how the world experiences design. Hey, g'day, mabuhay, kia ora, 你好, hallo, vítejte! We’re looking for current PhD students ready to bring their research into the real world and help shape the culture of AI at Canva. Our full-time, 16 week AI Research Internship starts in September. During your internship, you’ll work directly with Canva’s AI team on a live, industry-scale project, turning part of your PhD journey into real world impact. You’ll gain hands on experience with real data, production infrastructure and real deadlines, while learning from and working alongside the researchers and engineerings creating Canva’s next generation of AI-powered experiences. Where and how you can work This role is based in Sydney, and we’re looking for someone who calls it home. Our hybrid way of working gives you flexibility - you’ll have the option to work from home as well as connecting and collaborating with your team in-person, on campus. We trust teams to choose the balance that empowers them to achieve their goals. What you’d be doing in this role As Canva scales change continues to be part of our DNA. But we like to think that's all part of the fun. So this will give you the flavour of the type of things you'll be working on when you start, but this will likely evolve. At the moment, this role is focused on: Task-specific parameter pruning of foundational video generative models. Combining pruning with complementary optimisation techniques (e.g., DMD, LoRA-based methods). Measuring compute cost, latency, and quality trade-offs with rigorous evaluation. Collaborating with research, engineering, and product teams to move findings toward production. Contributing to the broader research community through publication where results support it. You’re probably a match if you have: You’re currently completing a PhD (ideally third year or later). You have experience training generative models. You can design, run, and interpret machine-learning experiments