Description
Zendesk's Custom Agents group enables customers to build specialised AI agents that understand business context, reason through complex tasks, and take actions across Zendesk and connected systems.
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.
Responsibilities
- 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 behaviour. Partner with Security and Privacy teams on permission-aware actions, prompt injection defences, 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.
Requirements
- 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 behaviour, 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 prioritise 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 rigour, delivery speed, and customer needs.
Nice to Have
- 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.