Senior Machine Learning Engineer

Zendesk Zendesk · Enterprise · Melbourne, Australia +1 · Remote

Zendesk is seeking a Senior Machine Learning Engineer to build and research advanced AI agents for their Resolution Platform. The role focuses on creating high-performance runtimes for AI agents that can Perceive, Reason, and Act, by designing sophisticated memory structures, autonomous learning mechanisms, and agentic workflows. Key responsibilities include optimizing agents for complex multi-step tool-use benchmarks, developing advanced memory and cognitive architectures, refining reasoning through trajectory analysis, architecting self-improving loops, engineering enterprise guardrails and content safety measures, implementing supervisor patterns for governance, and building rigorous continuous evaluation frameworks. The ideal candidate has a deep foundation in ML, transformer architectures, applied agentic systems, and a strong understanding of cognitive systems and safety, with a data-driven approach to evaluation.

What you'd actually do

  1. Building the World's Best Task Agents: You are tasked with achieving state-of-the-art performance against the industry's most rigorous benchmarks. You will optimize our agentic workflows to push past the 2026 frontiers, targeting elite-level autonomous problem solving on complex multi-step tool-use benchmarks.
  2. Advanced Memory & Cognitive Architectures: You will design sophisticated memory systems inspired by human cognition, allowing agents to dynamically filter interference, maintain context, and leverage long-term historical knowledge effectively across extended interactions.
  3. Trajectory Analysis & Reasoning Refinement: You will analyze complex agentic AI trajectories and trace patterns to understand how models navigate non-deterministic, multi-step problems. By leveraging these insights, you will build systems that capture the agent's entire chain-of-thought, analyzing conditional branches to actively detect anomalies and halt hallucination loops prior to failure.
  4. Self-Improving Loops & Skill Discovery: You will architect scalable, autonomous self-improving loops that allow agents to operate and learn continuously without human intervention. You will design frameworks where tool search patterns, error handling, and sophisticated retry logics are actively fed back into the system to dynamically improve the structure of the agentic AI planner. This includes enabling agents to autonomously discover, synthesize, and incrementally acquire new reusable skills based on environmental feedback and task completion.
  5. Enterprise Guardrails & Content Safety: You will engineer multi-layered defenses to secure agentic workflows against unique risks such as tool misuse, cascading action chains, and unintended control amplification. This includes designing strict input validation to block malicious prompt injections or jailbreak attempts, as well as output filtering to ensure responses remain within the application's domain boundary.

Skills

Required

  • Python
  • PyTorch
  • applied agent frameworks (e.g., LangChain, LangGraph, or similar orchestration tools)
  • designing real-world AI applications
  • leveraging agentic frameworks
  • building reliable, multi-step automated workflows
  • orchestrating specification, adaptive planning, tool execution, and iterative synthesis
  • embedding strict policies directly into the agent loop
  • decomposing high-level goals into actionable, verifiable sub-tasks
  • science of evaluation
  • closing the distribution mismatch between sandbox and production performance

Nice to have

  • transformer architectures

What the JD emphasized

  • multi-step tool-use benchmarks
  • multi-step problems
  • multi-agent coordination
  • multi-turn evaluation frameworks
  • multi-layered defenses
  • multi-agent coordination
  • multi-turn evaluation frameworks
  • multi-layered defenses
  • multi-step automated workflows

Other signals

  • building autonomous agents
  • multi-agent coordination
  • tool use
  • agentic workflows
  • enterprise guardrails
  • governance