Join the team redefining how the world experiences design.

Servus, 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, Australia but Austria is home to part of our European operations. And you have choice in where and how you work, we trust our Canvanauts to choose the balance that empowers them and their team to achieve their goals.

Fun fact, a big part of our Austrian operations is developing the AI product within Canva to help reimagine how artificial intelligence can be used in design. Pretty cool ha!

At Canva, our mission is to empower the world to design. We’re building AI that feels magical and lands real impact for millions of people - helping anyone create with confidence. We’re looking for a Machine Learning Engineer with strong Research Engineer / Applied Scientist instincts to bridge cutting-edge research and production systems, owning the pipelines, tooling, and experimentation loops that turn ambitious ideas into scalable, shippable reality.

About the team:

The Design Generation team builds machine learning systems that generate and enhance graphic designs directly in the Canva editor. We combine research and engineering to make complex design tasks simple and accessible for everyone. The team includes Research Scientists and Machine Learning Engineers working closely with backend, frontend, and platform teams. This role sits at the intersection of research and engineering: sometimes leaning into applied research and hypothesis testing, other times taking deep ownership of reusable training, inference, and evaluation pipelines that multiple teams depend on.

You’ll play a key role in:

• Standardising and scaling evaluation, training, and data pipelines

• Helping research ideas move quickly from prototype to production

• Ensuring our solutions fit coherently into Canva’s broader AI and product stack

By building durable foundations and enabling fast iteration, you’ll directly support Canva’s vision to empower the world to design.

About the role:

As a Machine Learning Engineer, you’ll partner closely with Research Scientists to test hypotheses quickly, while also owning the engineering work required to make those ideas reliable, reusable, and production-ready.

You’ll take responsibility for shared pipelines and infrastructure that power multimodal generative systems—helping unblock research velocity, reduce duplicated effort, and improve system performance at scale. Your work will directly influence the quality, speed, and reliability of AI-powered design features used by millions of Canva users.

What you'll do in the role:

• Partner closely with Research Scientists on multimodal generative AI, translating research ideas and hypotheses into practical, testable systems

• Own and evolve reusable training, inference, and evaluation pipelines, working across teams to standardise where possible

• Convert experimental Python research code into scalable, maintainable, and testable production code

• Design, build, and maintain large-scale data and evaluation pipelines that support rapid experimentation and reliable comparisons

• Support fast hypothesis testing by enabling lightweight experiments and clear evaluation signals

• Optimise models and pipelines for real-world constraints, including performance, latency, cost, and reliability

• Collaborate with stakeholders across Canva (including other AI teams) to align on shared approaches and avoid duplicated effort

• Stay ahead of industry trends and translate cutting-edge AI research into actionable product features

• Contribute to team roadmaps by identifying data, evaluation, or infrastructure bottlenecks and proposing solutions

You're likely a match if you have:

• Strong software engineering skills in Python, with experience building production-grade ML systems

• Experience owning training, inference, and evaluation pipelines for machine learning models

• Experience with RGBA data and layered image representations

• Hands-on experience with large-scale ML data workflows (e.g. Ray or similar frameworks), including data loading, batching, sharding, and versioning

• Solid understanding of ML training requirements—you know what a “good system” looks like and can anticipate downstream issues

• Experience working with cloud infrastructure (AWS) and distributed storage systems

• Ability to operate comfortably in ambiguous problem spaces, balancing research exploration with engineering rigour

• Strong communication skills and a collaborative mindset—you can work effectively with researchers, MLEs, and software engineers across disciplines

• A collaborative approach, comfortable taking ownership and iterating quickly.

Nice to have:

• Experience working with multimodal data (e.g., image–text pairs, design assets).

• Experience building synthetic data generation pipelines.

• Experience building impactful end-to-end demos that showcase research impact.

• Familiarity with evaluation frameworks, data quality metrics, and model monitoring systems.

• Prior research experience, including authorship or co-authorship of research papers, or contributions to open-source datasets, benchmarks, or ML tooling.