Senior Machine Learning Engineer - AI Enablement (AU remote)
CanvaSydney,Posted 2 April 2026
Tech Stack
Job Description
About the Group/Team
Canva's AI Platform Group serves as the foundation for AI innovation across the company, and achieving our GenAI ambitions depends on its ability to scale effectively. Within this group, the AI Enablement teams provide critical solution expertise to researchers and AI builders globally, helping them navigate platform capabilities, apply expert domain knowledge, optimise implementations, and accelerate the journey from experimentation to production. These teams bridge the gap between cutting-edge research and reliable, production-ready features, ensuring that innovation translates into real user value.
About the Role/Specialty
Please note, this is not a model building role This role sits at the intersection of solution expertise and platform engineering, requiring deep technical expertise across ML domains combined with strong platform knowledge. The senior MLE will context-switch between facilitating model evaluations, data pipeline, optimising inference performance and working directly with product teams building solutions. The nature of enablement work demands someone who operates as a force multiplier, finding satisfaction not just in solving technical problems but in enabling others to move faster. The role operates in a high-velocity environment where priorities shift rapidly based on team needs, requiring both technical excellence and pragmatic judgment. This combination of deep expertise and adaptive problem-solving is critical to accelerating the delivery of GenAI capabilities across Canva, removing friction from the path to production and ensuring product teams across the company can ship AI-powered features at the pace and quality our ambitions require.
What you’ll do (responsibilities):
Responsibilities will evolve as Canva's needs change, but here's what the role will focus on initially:
Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, labelling, and optimisation
Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions
Provide expert guidance on platform capabilities, helping teams understand what's possible and how to get there efficiently
Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations
Debug and resolve complex issues, identifying root causes and sharing learnings broadly
Balance hands-on problem solving with strategic thinking about how to scale enablement impact across the organisation
You're probably a match if you have any of the following:
Strong Python expertise with experience in ML frameworks
Experience working with Kubernetes
Experience or knowledge of building data pipelines
Experience or knowledge of model evaluations
Experience with inference optimisation and serving infrastructure
Experience supporting or working closely with research, data science, or product teams
Excellent debugging and problem-solving skills across the full ML stack
Ability to context-switch effectively across multiple concurrent projects
Strong communication skills with the ability to explain complex technical concepts to varied audiences
A collaborative mindset with a focus on enabling others to succeed
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 ... (truncated, view full listing at source)
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