About the team
The mission of Canva Research is to develop the AI technology that powers Canva's Creative Operating System, empowering everyone to design. We bridge cutting-edge research and applied methods with real-world challenges, building the technology that enables Canva, as an AI-native creative platform, solving today's problems and shaping tomorrow's.

Formed in June 2025, Canva Research has grown fast with teams across Sydney, London, Vienna, San Francisco and Beijing. We work across generative image, video, audio, agents, evaluations, on-device and foundation models, all with the same goal: turning research breakthroughs into features that make design more accessible, more capable and more fun for everyone who uses Canva.

About the role
Canva's generative models are judged by millions of people who'll never read a benchmark. They just know whether the design/output looks right. Turning that judgement into something measurable and something we can train against is one of the hardest problems in our research stack.

You'll build the systems that make that measurement possible: agentic evaluators, MLLM-as-judge pipelines, and the benchmarking infrastructure that tells us whether a model actually got better. This is a hands-on engineering role. You'll take evaluation research on reward modelling, preference learning, and inference-time alignment and turn it into practical, production-ready systems that give research teams a fast, reliable feedback loop.

What you'll do

• Design, build and optimise the infrastructure for MLLM-as-a-judge evaluation systems, giving research teams scalable, automated feedback

• Implement and experiment with inference-time alignment techniques, including prompt engineering, RAG and in-context learning, to directly improve model output quality

• Build and maintain benchmarking pipelines that compare internal and external models on design-centric tasks, watching for benchmark saturation and contamination that would quietly stop the numbers meaning anything

• Build infrastructure for reward modelling and preference learning that feeds post-training (RLHF, RLAIF) and inference-time selection

• Analyse evaluation data to identify model failure modes, and turn that into clear, actionable recommendations for the research team

• Run offline evaluation suites and online monitoring, with regression detection that catches quality drops before they reach users

• Collaborate with research scientists and ML engineers to integrate the agentic judge system into the model development lifecycle

• Translate the latest research in LLM evaluation and agentic AI into practical, production-ready engineering solutions

You're likely a match if you have

• A strong understanding of generative AI models (diffusion models, GANs, transformers, VLMs/MLLMs) and their architectures, deep enough to build evaluation systems that hold up

• Practical experience building or working with agentic AI systems, including MLLM-as-a-judge or similar automated evaluation approaches

• Experience with inference-time alignment techniques: prompt engineering, RAG, in-context learning

• Experience with reward modelling, preference learning, or alignment methods involving human feedback

• A solid understanding of machine learning fundamentals, hands-on experience with PyTorch, and the ability to optimise training and inference code for speed

• Experience managing or optimising large-scale distributed model training or evaluation runs across hundreds of GPUs

• Disciplined coding practices, comfortable with code review and pull requests

• Familiarity with evaluation libraries and frameworks

Nice to have

• Experience linking evaluation metrics to downstream product or business outcomes, and diagnosing why they diverge

• Knowledge of data visualisation tools to communicate findings effectively

• A background or interest in human-computer interaction, design principles, or AI ethics

What's in it for you?
Achieving our crazy big goals motivates us to work hard - and we do - but you'll experience lots of moments of magic, connectivity and fun woven throughout life at Canva, too. We also offer a range of benefits to set you up for every success in and outside of work.

Here's a taste of what's on offer:

• Equity packages - we want our success to be yours too
• Health benefits plans to support you and your wellbeing
• 401(k) retirement plan with company contribution
• Inclusive parental leave policy that supports all parents & carers
• An annual Vibe & Thrive allowance to support your wellbeing, social connection, office setup & more
• Flexible leave options that empower you to be a force for good, take time to recharge and supports you personally

Check out lifeatcanva.com for more 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 salary range for this position is $220,000 - $280,000. When calculating offers, we make salary decisions based on market data and candidates' skills and experience.

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.