Machine Learning Engineering Manager - Evaluations at Canva

As Canva grows, so does the impact and opportunity of our AI-powered features. We're looking for a Machine Learning Engineering Manager to coach a team of world-class Research Scientists and Machine Learning Engineers, build production-ready evaluation systems, and turn cutting-edge ML capabilities into delightful product experiences. If you thrive in bridging rigorous engineering with practical application, and you love helping others grow whilst solving hard technical problems - this could be the role for you. About the Role: You will lead and grow a team of high-performing Machine Learning Engineers and Research Scientists (EU based) who are advancing the future of AI at scale. Your focus will be on setting strategic technical direction, coaching others to deliver impactful engineering solutions, and ensuring the deployment of robust, scalable ML systems into production. You'll champion both engineering excellence and measurable impact, bridging foundational model capabilities with real-world deployment across Canva's platform. This is a hands-on leadership role for someone who is passionate about cultivating talent, shaping a technical vision, and partnering cross-functionally to embed cutting-edge AI into delightful user experiences. At the moment, this role is focused on: • Coaching and mentoring a high-performing team of Machine Learning Engineers and Research Scientists. • Owning the evaluation infrastructure - Design, build, and maintain robust evaluation systems, quality metrics, safety monitoring, red-teaming, competitive benchmarking - to guarantee enterprise readiness and user delight at scale. • Building automated metrics that reliably predict human aesthetic judgment across dimensions like visual hierarchy, layout coherence, typography, and brand alignment. • Advising on human evaluation pipelines and closing the loop between user signals and model improvements. • Setting technical strategy in alignment with Canva's AI and product goals. • Guiding engineering direction across model deployment, evaluation infrastructure, and production systems. • Partnering cross-functionally to ensure ML capabilities translate into reliable product impact. You're probably a match if you: • Have led machine learning engineering teams, with a strong track record of coaching and delivering production systems. • Possess expert knowledge in deploying and scaling generative models (Diffusion, GANs, VAEs, LLMs) in production environments with a strong focus on visual models (image, video, design). • Bring hands-on experience building ML infrastructure, evaluation pipelines, and monitoring systems at scale. • Excel at creating data-driven evaluation methodologies, turning user analytics and production metrics into clear, actionable insights. • Have strong systems design skills and experience with MLOps, model serving, and production reliability. • Have experience with visual quality assessment, aesthetic modelling, or human preference learning – bonus if you've tackled the gap between automated metrics and human raters. • Understand design principles (hierarchy, balance, typography, colour theory) well enough to operationalise them as measurable signals. • Thrive in collaborative environments and communicate clearly with technical and non-technical audiences. • Stay current with both SOTA research trends and engineering best practices, energised by continuous learning.