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Xero is seeking an Engineering Manager to lead a high-performing team within the Machine Learning & AI division, responsible for building and operating AI-powered experiences and capabilities across the platform. The role spans traditional machine learning (predictive models, intelligent automation) through to LLM-powered and agentic AI experiences.
Key responsibilities include:
- Leading a team of engineers building customer-facing AI experiences, intelligent product workflows, and backend services/platforms/infrastructure that power them
- Translating research, models, and emerging AI capabilities into reliable, secure, and scalable production products for millions of small business users
- Balancing predictable delivery with engineering quality and system health, building maturity across MLOps, LLMOps, observability, and evaluation practices
- Championing AI-native engineering practices and helping the team adapt design, build, test, and operational approaches
- Working closely with senior individual contributors (staff and principal engineers) to shape technical direction while focusing on team growth, delivery excellence, and sustainable practices
The team works on production ML and AI engineering platforms, JAX (Xero's conversational GenAI assistant), intelligent financial workflows (automated bank reconciliation, smart document capture), XeroForce (AI-powered workflow platform), and end-to-end AI product experiences integrated into customer-facing workflows.
You'll report to the GM of ML Engineering within the Data & AI organisation and partner closely with Applied Science, Data Engineering, Product, and other engineering teams.
The company supports a hybrid working model with flexibility to balance remote work and in-office collaboration.
Requirements:
- Strong track record of leading, coaching, and developing engineers; building team capability and creating an environment where people can do their best work
- Strong technical judgement across architecture, system design, and engineering trade-offs; ability to guide decisions across broad and evolving technology landscapes
- Experience translating business goals into executable engineering plans, balancing near-term delivery with long-term technical health and maintainability
- Proven ability to deliver roadmaps predictably in fast-paced, evolving environments; comfortable navigating ambiguity and adapting as priorities and technology change
- Understanding of building and operating production-grade software, from customer-facing experiences and backend services through distributed systems, platforms, and infrastructure
- Hands-on capability: comfortable writing, reviewing, or debugging production code to unblock the team, support incidents, or resolve complex technical challenges
- Experience with machine learning, generative AI, or AI-powered products is a bonus; genuine curiosity about the space and appetite to learn how these technologies are changing software engineering is valued
