PhD Research Scientist Intern - Reinforcement Learning for Diffusion Modelling

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.

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. This gives you a flavour of the work you'd start with, and it will likely evolve over time. At the moment, this role is focused on:

• Designing and validating a rubric-guided, per-layer VLM judge for RGBA layer decomposition, calibrated against human evaluations.

• Building VLM-based methods for automatic, human-aligned evaluation of multi-layer designs.

• Turning VLM-based evaluators into reward functions to train generative models in a reinforcement learning setting.

• Distilling those judges into lightweight reward models that score layered images from learned representations, at a fraction of the inference cost.

• Collaborating with research, engineering, and product teams to move findings toward production and Canva's layered-generation roadmap.

• Contributing to the broader research community through publication where results support it.

The team builds the groundwork before you arrive — baselines reproduced, harnesses running, data prepared. That means you start on the novel parts in week one rather than spending a month on setup.

You're probably a match if

• You're currently completing a PhD, ideally third year or later.

Nice to have

• PyTorch at scale, and the ability to write research code for data processing, training and evaluation.

• Multi-GPU training (FSDP, DeepSpeed) and evaluation-harness engineering.

• Layered or RGBA generation, matting, or inpainting experience.

• Familiarity with reward-hacking and score-compression diagnostics, or human-evaluation design.

• Publications or open-source contributions in generative modelling, RLHF, or multimodal models.

What you should aim to take away

• Publishable and patentable contributions based on the work you’ve done

• A paper draft covering said work, with support on publication strategy.

• Compute, base checkpoints, preference data and annotation budget, provided.

• Four mentors: a coach for weekly 1:1s, plus a specialist lead on each workstream.

• Work that feeds directly into a product used by hundreds of millions of people.

Other stuff to know

We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.

We celebrate all types of skills and backgrounds at Canva so even if you don’t feel like your skills quite match what’s listed above - we still want to hear from you!

Please note that interviews are conducted virtually.