Senior Machine Learning Engineer

About the Role

Join the Brand Templates team as an ML technical lead to build intelligent, AI-powered features that make it effortless for organizations to create and scale on-brand content.

Responsibilities

• Own the end-to-end ML lifecycle from problem framing to production deployment and iteration.
• Develop ranking and recommendation models to identify high-performing designs for Brand Templates.
• Build brandification pipelines to automatically transform templates to conform to brand guidelines.
• Develop layout extraction and understanding systems for Canva's design format (CDF).
• Design and productionize LLM-based pipelines for metadata generation.
• Run experiments to validate model effectiveness and measure impact.
• Collaborate with product managers, designers, and engineers to integrate ML features.
• Establish ML best practices within the team, including experiment tracking, evaluation, monitoring, and documentation.

Requirements

• 5+ years of hands-on experience building and deploying ML-powered features in production.
• Proficiency in Python and ML frameworks (PyTorch or TensorFlow).
• Strong experience with NLP/NLU techniques (LLMs, embeddings, semantic search, prompt engineering, RAG, fine-tuning).
• Experience with document understanding, layout analysis, or structured data extraction.
• Experience building information retrieval, ranking, or recommendation systems.
• Skilled across the ML lifecycle: data processing, model training, evaluation, deployment, and monitoring.
• Experience designing and running A/B experiments.
• Ability to operate independently as an ML technical lead within a product team.
• Strong product mindset focused on user experience and measurable business outcomes.
• Commitment to scalable, maintainable ML systems with clear metrics.
• Disciplined coding practices and participation in code reviews.

Nice-to-Haves

• Experience with layout understanding or structured extraction from design/document formats.
• Familiarity with embeddings and vector databases.
• Experience in enterprise or B2B product contexts.
• Familiarity with GenAI platforms (e.g., OpenAI, Anthropic).
• Experience with microservices architectures and large monorepos.
• Master's or PhD in a machine learning discipline.