Senior Engineering Manager, Machine Learning in Melbourne

About the group

Zendesk’s Custom Agents group enables customers to build specialized AI agents that understand business context, reason through complex tasks, and take actions across Zendesk and connected systems. These agents combine natural language instructions, enterprise knowledge, and tools to automate work such as investigating requests, interpreting documents, applying policies, and coordinating service workflows. Our focus is making these capabilities reliable, configurable, and practical for real business operations.

About the role

We’re looking for a Senior Manager, Machine Learning to lead the development of the ML capabilities behind Custom Agents. You will set technical direction, build and develop a strong team, and turn advances in generative AI into dependable customer experiences. This role combines people leadership, applied ML judgment, and accountability for production delivery. You will partner closely with Product, Engineering, Design, ML Science, and the Agent Platform team to improve how agents reason, use tools, follow policies, and complete tasks. You will guide decisions across model quality, reliability, latency, and cost, connecting technical investments to measurable customer value.

What you’ll do

• Lead and grow the team. Hire, coach, and develop ML engineers and scientists. Build a culture of ownership, thoughtful experimentation, inclusion, and technical excellence, while developing the team’s next generation of leaders.
• Set the ML strategy and roadmap. Translate customer needs and product goals into clear priorities, investment decisions, and achievable milestones. Balance foundational improvements with delivery of new capabilities.
• Improve agent reasoning and execution. Guide work on planning, tool selection, context management, knowledge grounding, and structured outputs so agents consistently complete the intended task.
• Establish rigorous evaluation. Develop representative benchmarks, human annotation processes, automated scorers, and regression tests. Measure task completion, tool-use correctness, policy adherence, and whether reported outcomes match actual execution.
• Own quality throughout the lifecycle. Set standards for experimentation, validation, release readiness, and production monitoring. Use execution traces and customer feedback to diagnose failures and drive improvements.
• Optimize performance and economics. Evaluate models and agent architectures against quality, latency, and cost. Make pragmatic decisions about model selection, routing, fallbacks, and inference efficiency.
• Build trustworthy agent behavior. Partner with Security and Privacy teams on permission-aware actions, prompt injection defenses, data handling, and appropriate escalation or human approval.
• Partner across the platform. Adopt and help shape shared capabilities for simulation, observability, experimentation, and durable execution. Work with adjacent teams to integrate Custom Agents into broader service workflows.
• Deliver measurable customer impact. Connect ML improvements to successful automation, reduced handling time, customer adoption, and trust. Communicate progress, tradeoffs, and risks clearly to stakeholders.

What you bring

• A demonstrated track record of leading ML or AI teams and delivering production systems with measurable customer impact.
• Strong people management skills, including hiring, coaching, performance management, and developing technical leaders.
• Deep understanding of modern ML and generative AI systems, with practical experience taking LLM applications from experimentation into production.
• Technical judgment across model behavior, software architecture, evaluation, and operational reliability, with the ability to guide design reviews and assess experimental results.
• Experience establishing meaningful quality metrics and using data to prioritize improvements and make release decisions.
• The ability to lead through ambiguity, coordinate multiple workstreams, and influence partners across Product, Engineering, Science, and Security.
• Clear communication and a pragmatic approach to balancing scientific rigor, delivery speed, and customer needs.

Additional experience we value

• Building agents that use tools, retrieve enterprise knowledge, or coordinate workflows across systems.
• Working with LLM evaluation, human feedback, multimodal document processing, or agent safety.
• Leading distributed teams or delivering ML products in enterprise SaaS, customer service, or business automation.

Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.

Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law.