PhD Research Scientist Intern Reinforcement Learning, Images

Join the team redefining how the world experiences design.

Hey gday mabuhay kia ora 你好 hallo vítejte!

Were 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 youll work directly with Canvas AI team on a live industry-scale project turning part of your PhD journey into real world impact.

Youll gain hands on experience with real data production infrastructure and real deadlines while learning from and working alongside the researchers and engineerings creating Canvas next generation of AI-powered experiences.

What youd be doing in this role

As Canva scales change continues to be part of our DNA but we like to think thats all part of the fun. This gives you a flavour of the work youd 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 Canvas 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.

Youre probably a match if

• Youre currently completing a PhD ideally third year or later.

• A strong diffusion or flow-matching backgroundwith hands-on policy-gradient RL for generative models (GRPO PPO DPO or similar).

• Experience fine-tuning VLMs (e.g. with LoRA) and designing prompts or rubrics for evaluation tasks.

• Reward modelling experience preference optimisation pseudo-labelling distillation.

• You can read a recent paper and reproduce it quickly.

• You communicate technical work clearly in writing and in presentations.

• You enjoy working closely with researchers and engineers on hard problems.

• Juggle several threads at once drop into a new one without losing the last

• Set your own priorities on a daily basis and between checkpoints

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 youve 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.

Additional Information :

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 dont feel like your skills quite match whats listed above - we still want to hear from you!

Please note that interviews are conducted virtually.

Remote Work :

No

Employment Type :

Intern