Staff Machine Learning Engineer - Integrations & Solutions Group (au Remote)

Canva Canva · Enterprise · Sydney, Australia · Information Technology

Staff Machine Learning Engineer responsible for building and evolving Canva's AI integration layer, connecting Canva's design capabilities to leading AI assistants. This involves defining AI-ready tools and APIs, owning evaluation frameworks, shaping agent architecture, building observability systems, and influencing integration strategy with AI partners.

What you'd actually do

  1. Drive the design and evolution of AI-ready tools and APIs that enable LLM platforms (ChatGPT, Claude, Gemini and others) to reliably interact with Canva's design capabilities — defining the patterns and standards that other teams adopt for tool descriptions, payload structures, and intent-based interfaces. Pioneer agent-to-agent communication approaches.
  2. Own and evolve evaluation frameworks that systematically measure tool-use accuracy across platforms — defining what "good" looks like for proxy-based fast evals and real-client production evals, and ensuring these frameworks scale as we add platforms and capabilities.
  3. Shape Canva's agent architecture — making strategic technical decisions about where intelligence should live (in external LLMs vs Canva-hosted agents), building the orchestration layers that allow third-party providers to invoke Canva's design tools at scale, and driving automation of complex workflows like marketing campaigns.
  4. Define and build observability systems that give multiple teams visibility into how AI assistants consume Canva's tools in production — identifying failure patterns, setting quality benchmarks, and closing the loop between production data and continuous improvement.
  5. Work across team and platform boundaries — proactively identifying problems not yet defined, understanding behavioural quirks across LLM platforms, and driving solutions that span the AI Integrations, API Capabilities, and Workflow Integrations teams.

Skills

Required

  • Python
  • ML frameworks
  • TypeScript/Node.js
  • Cloud infrastructure (Cloudflare Workers, AWS, or similar)
  • Experience shipping LLM-powered systems
  • Experience building/owning evaluation pipelines
  • Experience setting technical standards
  • Experience connecting ecosystem changes to strategy

Nice to have

  • Experience with agent-to-agent communication
  • Experience with proxy-based fast evals
  • Experience with real-client production evals
  • Experience with orchestration layers for third-party providers
  • Experience with automation of complex workflows

What the JD emphasized

  • shipped LLM-powered systems into production and can quantify your impact
  • proactively find and solve problems others haven't defined yet
  • built or owned evaluation pipelines end-to-end
  • set technical standards that others follow
  • connect external ecosystem changes to internal strategy

Other signals

  • building agent architecture
  • integrating with LLM platforms
  • defining technical standards for AI integrations
  • owning evaluation frameworks for AI tools
  • driving automation of complex workflows