Join the team redefining how the world experiences design.

Hey, g'day, mabuhay, kia ora,你好, hallo, vítejte!

Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point. 

Where and how you can work

Our flagship campus is in Sydney. This role is preferred to be based out of our Sydney office, where the majority of this team is located. 

About the Group/Team

Canva Research – Design Generation

We’re building the systems that turn imagination into reality. Within Canva Research, the Design Generation group focuses on creating AI models and pipelines that produce beautiful designs for Canvas users. We work at the intersection of research and product, partnering closely with Product, Design, and Engineering to ship AI-powered experiences that feel magical, reliable, and deeply human. Our work directly shapes how users create, explore, and express ideas in Canva every day.

About the Role/Specialty:

As a Senior Research Engineer, we sit at the boundary between research and production. We explore new ideas in generative modelling, evaluate their real-world impact, and turn promising research into scalable systems that power Canva’s design experiences. As a Senior Research Engineer, this role operates with a high degree of autonomy, solving complex problems, influencing technical direction, and collaborating across teams to deliver meaningful outcomes. Our success is measured not just by research quality, but by the impact our work has on real users.

What you’ll do (responsibilities):

• You’ll design, experiment with, and evaluate generative AI models for design-focused use cases.

• You’ll translate research outcomes into scalable, production-ready systems.

• You’ll collaborate closely with Product, Design, and Engineering partners to align research with user needs.

• You’ll analyse model performance using both quantitative metrics and qualitative user signals.

• You’ll contribute to technical discussions, architecture decisions, and long-term AI strategy.

• You’ll share learnings through documentation, reviews, and internal presentations to uplift the broader team.

What we're looking for:

You’re an accomplished applied researcher who loves seeing ideas land in the hands of real users. You’re comfortable navigating ambiguity, balancing research depth with practical constraints, and making thoughtful trade-offs. You bring solid experience in machine learning or generative modelling, enjoy collaborating across disciplines, and care deeply about quality, ethics, and impact. You’re motivated by solving meaningful problems at scale and excited by the challenge of building AI that empowers creativity.

• Bachelor or Masters degree in Computer Science, Computer Engineering or a related field, or equivalent skills gained through 3-5 years of work experience. Advanced qualifications including a PhD are an advantage.

• Experience with generative models: Proficient in working with large language models (LLMs), diffusion models, and multimodal architectures to develop innovative solutions.

• Skills in training generative models: Experienced in techniques such as fine-tuning, in-context learning, and reinforcement learning to optimise model performance.

• Proficiency in deep learning frameworks: Skilled in leveraging tools like PyTorch, JAX, and HuggingFace for advanced research and experimentation.

• Expertise in experimental design and evaluation: Capable of implementing experimental design strategies for generative models, using both quantitative metrics and qualitative analysis to assess outcomes.

• Ability to enhance research tooling and workflows: Experienced in rapid prototyping, conducting ablation studies, and tracking experiments to streamline research processes.

• Competence in data curation and analysis: Adept at managing and analysing datasets for training and evaluating generative systems.

• Familiarity with current research literature: Skilled at reproducing impactful papers and applying novel techniques to address emerging problem spaces.