Description
At Canva, we're building a future powered by AI that's as magical as it is impactful.
As an LLM Agent Research Scientist at Canva, you'll advance the frontier of agentic AI,building autonomous systems that can reason, plan, use tools, and act on behalf of hundreds of millions of users.
You'll explore the boundaries of what agents can achieve in real-world productivity and creative scenarios, and bring that research into Canva's core product experiences,across design, presentations, documents, spreadsheets, video, and other multimodal creation and collaboration workflows.
About the Group/Team
We're the CORE team within the Generative AI supergroup. Our mission is to invent foundational technologies that will power the future of AI-assisted design. From large-scale models to groundbreaking research, our team builds the technical core of Canva's creative intelligence engine. We collaborate globally to ship research that makes a real impact,from smart editing to AI video tools,at massive scale.
Responsibilities
At the moment, this role is focused on:
- Designing and advancing state-of-the-art generative AI models across multimodal domains
- Improving AI efficiency across the stack, including:
- Kernel and graph optimization
- Model compilation and systems optimization
- Model compression techniques (quantization, distillation)
- Efficient model architecture design
- Scalable inference systems
- Collaborating cross-functionally with research, engineering, and product teams to bring innovations into production
- Translating research breakthroughs into impactful features within Canva’s core products
- Contributing to the broader AI community through publications at top-tier conferences
Qualifications
You’re probably a match if you have:
- Strong expertise in foundation models, particularly in generative AI (e.g., diffusion models, LLMs, VLMs)
- Experience in AI efficiency, such as:
- Low-level optimization (kernel/graph)
- Model optimization (quantization, distillation, compression)
- Efficient architecture design and inference systems
- A strong academic and/or industry track record, including publications in leading conferences (e.g., NeurIPS, ICML, ICLR, CVPR) or meaningful open-source contributions
- Proficiency in tools such as Python, PyTorch, Transformers, Diffusers, Megatron, DeepSpeed, and cloud platforms
- Clear communication skills and ability to collaborate effectively across teams