Machine Learning Engineer - Inference Platform (AU remote)
CanvaSydney,Posted 7 April 2026
Tech Stack
Job Description
About the Group/Team:
We are part of the Generative AI Supergroup, focused on building the foundations that enable Canva to become an AI-first creative partner. Within this, the AI Platform group is responsible for developing scalable infrastructure and tooling that powers machine learning across Canva. Our Inference Platform team sits at the heart of this mission—ensuring that ML models are deployed, served, and optimised efficiently at scale. We enable fast, reliable, and cost-effective inference so that every user interaction feels instant and magical. By building robust systems that support real-time AI features, we help unlock entirely new creative possibilities for our global community.
About the Role/Specialty:
As a Machine Learning Engineer, you’ll focus on building and improving systems that serve machine learning models in production. You’ll work closely with research scientists, backend engineers, and platform teams to ensure models are scalable, reliable, and performant. This role blends engineering excellence with practical ML knowledge—you’ll be implementing production-grade systems, improving latency and throughput, and contributing to infrastructure that supports Canva’s rapidly growing AI capabilities. Your work will directly impact how quickly and effectively users can access AI-powered features across the platform.
What you’ll do (responsibilities):
You’ll design, build, and maintain scalable systems for serving machine learning models in production
You’ll optimise inference performance, including latency, throughput, and cost efficiency
You’ll collaborate with ML researchers and engineers to productionise models
You’ll implement monitoring, logging, and alerting to ensure system reliability
You’ll contribute to platform tooling that improves developer experience and ML workflows
You’ll identify opportunities to improve infrastructure and drive technical enhancements across the platform
You’ll provide on call support to the services supported by the team
What we're looking for:
We’re looking for someone who is passionate about building reliable, scalable systems and is excited by the opportunity to work at the intersection of machine learning and infrastructure. You enjoy solving complex technical problems and are motivated by delivering real-world impact at scale. You likely have experience working with backend systems and some exposure to deploying or supporting machine learning models in production. You’re comfortable collaborating across teams and are eager to learn and grow in a fast-paced, product-driven environment. Most importantly, you bring a growth mindset and a desire to help shape the future of AI at Canva.
Don't tick all the boxes? Don't worry about that - nobody does!  
We’d still love to hear from you! At Canva, we know that great engineers come from a variety of backgrounds, and we value passion, curiosity, and a willingness to learn just as much as specific experience. If you're excited about this role but don’t tick every box, we encourage you to apply, you might a great fit in ways you didn’t expect!
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 stack 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 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 info.
Other stuff to know
We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When ... (truncated, view full listing at source)
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