PhD Research Scientist Intern - Edge AI

What you'll do Survey candidate video-capable VLMs (e.g. Gemma, Qwen-VL, SmolVLM, MiniCPM-V) and determine the best starting point Apply model optimization techniques and architecture improvements to specialize vision-language models for on-device deployment, including quantization, pruning, distillation, hardware-specific compilation, and task-specific fine-tuning for caption placement. Deploy the model on real, high-traffic mobile hardware through our on-device inference library, iterating the optimisation-deployment loop against real on-device measurements. Run comparative evaluation against at least one alternative optimisation path, and human evaluation against our server-side captions quality bar. Document your findings clearly enough that

the team can act on them, mapping which workloads are viable on-device today and which aren't yet, and why. Compile your output into a patent filing and a paper publication. You're likely a match if you have Strong Python and hands-on PyTorch experience, including training and fine-tuning vision-language models. A solid understanding of modern vision-language and multimodal architectures, with the ability to pick up a recent paper and reproduce it. Experience with optimisation methods like quantisation, pruning, or distillation, and a clear sense of what each costs you in accuracy. Experience deploying models on-device or at the edge with runtimes like Core ML, LiteRT/TFLite, ONNX Runtime, or ExecuTorch, working within real memory and latency budgets. Experience running your own research project end to end: making a plan, measuring carefully, and iterating on what you find. Current enrolment in a PhD in ML, CS, or a related field, with first-author papers at venues like CVPR, NeurIPS, ICCV/ECCV, ICLR, or ICML.

Nice to have Experience with video understanding models, ideally the token-efficient kind. Publications or open-source contributions in efficient ML, multimodal models, or edge AI. Experience writing custom kernels for inference optimisation. Experience deploying models across different on-device hardware accelerators (e.g. Apple Neural Engine, DSPs). Experience working across research and product teams, in industry or on a previous internship. 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.

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