Senior Machine Learning Engineer - Agent Tools Interop
Within AustraliaFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonJavaTypeScriptLLMLangChain
Requirements
- Hands-on production experience with LLM tool-use and function calling: designing tool schemas, understanding how model behavior is shaped by tool definitions, and shipping agentic integrations to real users.
- Built evaluation frameworks that measure AI feature quality systematically, and use those signals to drive improvement.
- Java proficiency is essential given our backend services infrastructure; Python or TypeScript is a strong plus.
- Experience at the boundary of ML and platform engineering, where you've had to collaborate deeply with backend or infrastructure teams to make AI integrations production-grade, safe, and scalable.
- Familiarity with MCP, LangChain, LangGraph, or agent frameworks is a real differentiator.
- Prompt engineering experience specifically for tool definitions and tool calling schemas.
Responsibilities
- Build and evolve the systems that enable agents to discover, invoke, and safely execute capabilities across Canva at scale, from initial foundations through to long-term platform maturity.
- Design tool schemas and definition patterns that maximize LLM tool selection accuracy and reliable invocation across diverse agent consumers and AI integrations.
- Build and operate evaluation pipelines that measure tool calling behavior in production, catch regressions, and drive continuous quality improvement.
- Collaborate with product, platform, and GenAI teams to integrate agentic capabilities into production systems and understand how tool use behaves at real-world scale.
- Advise contributing teams on how to define tools agents can reliably call, lowering the bar for onboarding new capabilities into the shared agentic layer.
- Partner with platform engineers on governance, safety, and execution guarantees, so the platform earns trust as a company-wide dependency for AI integrations.
- Stay close to developments in agentic AI (MCP, function calling, A2A protocols) and apply new approaches where they meaningfully improve tool interoperability.
- Mentor engineers on agentic integration patterns and evaluation methodology.
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