Staff Machine Learning Engineer - Recommendations (AU remote)

About the Group/Team: Canva is full of AI-powered magic, but our ML-powered Recommendations is one of the highest-traffic machine-learning system at Canva . Serving over two hundred million users every month, our recommendations help Canva users to find exactly what they’re looking for before they even know it. We are always working with cutting-edge technology, reading new research papers, and running dramatic experiments to improve our recommender systems. The Recommendations team is a great fit for someone looking to make a huge impact on Canva users by employing the latest in Machine Learning Research. A few of the Recommender Systems that we own: Recommending Canva templates to users to get them started with their design, helping them jump-start their creative process. “Magic Recommendations” for images, graphics, video and audio, enabling users to automatically find the perfect content to enhance their designs. Design-aware recommendations for images, graphics inside the Canva Editor. Recommendations for Canva Creators you might like to follow. Recommendations for Fonts you might like to use. And many more! About the Role/Specialty: At Canva , our Recommendations team is at the forefront of crafting personalized experiences that delight our users. As a Staff Machine Learning Engineer, you'll set the technical direction for our recommendation systems and lead the highest-impact bets that shape how Canva understands and anticipates user intent. We innovate by integrating the latest research into our recommendation algorithms, driving user engagement through highly relevant and timely suggestions. Your work will directly influence millions of users by enhancing their design journey, making your contributions pivotal to Canva ’s success. This role is perfect for someone with deep experience building production recommender systems at scale who is passionate about creating intuitive and effective user experiences. You will work closely with other engineers, data sci