Job description
Company Description
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 head office is in Sydney, Australia, but San Francisco is now home to our US operations. The role is listed as hybrid, meaning we are incredibly flexible and empower you to work where you prefer - whether that’s at home or at the office.
Job Description
About the role:
In your role as Senior Research (Machine Learning) Engineer, you’ll be at the heart of our mission to make advanced machine learning design tools easy for everyone to use. Your work will help create new features that make it simpler and more fun for Canva users to bring their ideas to life. By developing and integrating these cutting-edge technologies, you’ll help us achieve our goal of empowering the world to design. Your contributions will enhance user experiences, drive Canva’s growth, and keep us at the forefront of the design industry. Together, we’ll make a real impact and shape the future of design.
At the moment, this role is focused on:
- Partnering with Research Scientists on research in multimodal generative AI, translating theory into practical applications
- Porting experimental Python code to scalable, testable, and production-grade solutions, incorporating performance benchmarks and robust testing practices
- Designing, developing, and implementing innovative model architectures and algorithms for multimodal design generation
- Building sustainable ML pipelines that support continuous integration, deployment, and monitoring of generative models in production environments
- Optimizing and scaling models for efficiency, latency, and throughput across large distributed systems
- Enhancing and maintaining high-quality datasets and annotations to fuel multimodal learning
- Investigating and addressing production incidents through root cause analysis and sustainable fixes
- Collaborating closely with cross-functional stakeholders across Canva to build aligned, technically feasible, and high-impact solutions
You’re probably a match if you:
- Have deep experience developing generative AI models, including Diffusion Models, GANs, or Transformers, and can speak to their real-world application
- Have successfully managed and optimized large-scale distributed training (e.g. across 100s of GPUs) and understand the infrastructure trade-offs
- Bring a strong understanding of ML principles and have used frameworks like PyTorch to develop and optimize performant models
- Demonstrate solid engineering practices – clean code, rigorous testing, CI/CD workflows, and robust observability in production
- •re experienced working with cloud environments, ideally AWS, and understand how to scale ML systems in those contexts
- Are comfortable developing production-ready Python applications, and ideally have familiarity with Java (backend) or JavaScript (frontend) tech stacks
- Thrive in ambiguity, show end-to-end ownership of complex initiatives, and consistently drive for pragmatic solutions
- Communicate clearly, collaborate with kindness, and value knowledge-sharing and co-creation across teams
Additional Information
Other stuff to know
We make hiring decisions based on your experience, skills, merit and business needs, in compliance with applicable local laws. 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!
When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. Please note that interviews are conducted virtually.
At Canva, we value fairness, and we strive to provide competitive, market-informed compensation whilst ensuring internal equity within the team in each region. The target base salary range for this position is $220,000 - $260,000.
When calculating offers, we make salary decisions based on market data, your experience levels, and internal benchmarks of your peers in the same domain and job level.
