Senior Machine Learning Engineer - Inspire & Create (AU Remote)

Canva
Sydney,Posted 16 March 2026

Job Description

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. About the team We're Canva Grow, one of Canva’s newest product groups, born from the acquisition of MagicBrief. Our mission is to build the surface inside Canva that brings creativity, data, and insights together - helping businesses grow through better advertising. Advertising, when done well, connects people with things they love. Our goal is to help every business create ads that are effective, creative, and respectful of attention. We’re scaling fast, with the agility of a startup and the backing of Canva’s global platform. You'll be joining the Inspire & Create team, leading product development at the intersection of publishing systems, creative performance, and integrated ad platforms. But your work won’t be constrained to a single team. As our first MLE in Grow we will be looking to you to inform the full feedback loop across the whole group - your work will impact the entire customer journey from Inspire → Create → Publish → Insights → Recommend → Inspire. What you’d be doing in this role: As Canva scales change continues to be part of our DNA. But we like to think that's all part of the fun. So this will give you the flavour of the type of things you'll be working on when you start, but this will likely evolve. At the moment, this role is focused on: Building, testing, and shipping ML-powered features that enhance our ad generation experiences Designing scalable ML pipelines in collaboration with our platform teams Working closely with PMs, designers and engineers to align on user needs and product direction Running experiments to validate assumptions and improve model effectiveness Contributing to shared ML infrastructure and best practices at Canva Keeping up with state-of-the-art developments and exploring ways to apply them at scale You're probably a match if You’re a highly skilled machine learning engineer with a proven track record of delivering ML-powered product features in production. You bring deep technical expertise across the ML lifecycle - from data wrangling and model development to evaluation, deployment, and monitoring. You’re comfortable operating independently while collaborating deeply with cross-functional teams, and you’re motivated by user impact and product outcomes. You have demonstrated experience building and deploying ML-powered features in production environments. You have proficiency with Python and frameworks like TensorFlow or PyTorch. You are skilled across the ML lifecycle: data processing, model training, evaluation, deployment, and monitoring. You have proven ability to work independently while collaborating cross-functionally with product, design, and data. You have experience with Generative AI (GenAI): LLMs, diffusion models, prompt engineering, RAG, or fine-tuning. You have familiarity with GenAI platforms (e.g. OpenAI, Hugging Face, Anthropic) and embedding-based systems. You have a strong product mindset - prioritises ML solutions that improve user experience and product outcomes. You are committed to scalable, maintainable ML systems with clear metrics and impact tracking. You are passionate about ethical ML and responsible data usage, especially in learning environments. You enjoy mentoring and sharing best practices within the team.   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? ... (truncated, view full listing at source)
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