Senior Machine Learning Engineer - Training Platform (AU remote)

About the Group/Team We’re part of the Training Platform team within Canva’s AI Platform group, which sits in the Generative AI supergroup. Our team is responsible for the systems that power model training at scale, building the foundations that enable teams across Canva to create, train, and scale AI-powered experiences. Our focus is on building reliable, efficient, and developer-friendly training infrastructure — from orchestration and distributed training systems to experimentation and platform capabilities that support large-scale AI workloads. We enable teams across Canva to push the boundaries of what’s possible with AI. About the Role/Specialty As a Senior Machine Learning Engineer, you’ll focus on designing, scaling, and maturing the systems and infrastructure that support training workloads across Canva. You’ll work on a Kubernetes-based training platform that enables distributed AI workloads across a wide range of teams, frameworks, and use cases, while also contributing to the surrounding platform capabilities that support the end-to-end training lifecycle — such as experiment management, artifact management, and other core systems needed to run AI workloads reliably and at scale. You’ll help evolve these capabilities over time, improving their reliability, scalability, usability, and overall platform maturity. You’ll collaborate closely with research scientists, AI engineers, product teams, and cloud/infrastructure teams to ensure workloads can run efficiently, reproducibly, and reliably at scale. You’ll also help shape the roadmap for the platform by understanding user pain points, improving platform capabilities, and contributing to the long-term direction of Canva’s training infrastructure. This role is ideal for someone who enjoys working on the systems behind AI — not just the models themselves — and wants to have broad impact across multiple teams. What you’ll do (responsibilities) You’ll contribute to the evolution of Canva’s unified training platform for AI training workloads You’ll improve reliability, observability, debugging, and operational support for training systems You’ll design and build the platform capabilities that enable better scheduling at scale, including resource allocation, priority management, and quota management for training workloads. You’ll collaborate closely with research scientists, ML engineers, product teams, and cloud/infrastructure teams to improve training platform workflows and outcomes You’ll contribute to system design and architecture decisions across Canva’s AI Platform You’ll help shape platform roadmap and priorities based on user pain points, adoption needs, and long-term platform maturity You’ll mentor engineers and share best practices in AI systems and infrastructure What we're looking for You’re an engineer who loves building the systems that power AI at scale. You have strong experience in training pipelines, distributed systems, or large-scale AI infrastructure, and you’re excited by the challenge of making training workloads more reliable, scalable, and efficient. You bring strong experience working with Kubernetes and containerized workloads. Experience with training infrastructure, or distributed frameworks such as Ray, PyTorch distributed training, or similar technologies will be highly valuable. You’re also familiar with the modern cloud and infrastructure services that underpin high-performance AI workloads — for example, high-performance storage, HPC environments, fast interconnects and networking capabilities, or services such as FSx, EFA, and related infrastructure commonly used in large-scale training environments. You bring a strong sense of ownership and enjoy working on complex, cross-cutting problems that impact multiple teams. You’re comfortable collaborating with engineers, applied scientists, and infrastructure partners, and you care deeply about scalability, reliability, usability, and developer experience. Most importantly, you’re motivated by the opportunity to help Canva build the platform foundations that enable AI-powered creativity at scale. What the candidate will learn and how will they develop at Canva: Deep expertise in large-scale AI training systems, Kubernetes-based workload orchestration and execution, and distributed infrastructure Hands-on experience with modern AI training workloads at scale Exposure to the cloud, storage, and networking capabilities required for high-performance distributed training environments Opportunities to influence platform-wide architecture, roadmap, and AI Platform best practices Growth through collaboration with world-class ML engineers, applied scientists, and infrastructure specialists The ability to shape how AI is built and scaled across a global product 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 you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. All interviews are conducted virtually