Engineering Manager - Machine Learning (AI Products)

Xero; Auckland; On-site; NZD 180,000 - 240,000; Full time

Job summary

Xero is seeking an Engineering Manager to lead a high-performing team of ML and platform engineers, turning research into production-grade ML infrastructure at scale. You’ll balance delivery with system health, drive MLOps maturity and observability, and champion AI-native workflows across the full software lifecycle.

You’ll work with staff and principal engineers to shape architecture while focusing on team growth and delivery discipline, ensuring we run AI at Xero’s scale with rigor and care.

Qualifications

• You’ll bring a strong track record of leading, coaching and developing engineers, with real experience building team capability over time.
• Strong technical judgement across architecture, system design and engineering trade-offs.
• You’ve translated business goals into executable engineering plans, balancing short-term delivery against long-term system health.
• Delivering roadmaps predictably in fast-paced, evolving environments is familiar territory for you.
• You stay hands-on when it counts, comfortable writing, reviewing or debugging production code to unblock your team or support an incident.
• Experience with machine learning or AI systems is a bonus rather than a requirement, though genuine curiosity about the space will help you thrive here.

Responsibilities

• Lead, coach and develop ML and platform engineers to scale production ML infrastructure.
• Drive MLOps maturity, observability and reliability of AI systems in production.
• Collaborate with senior ICs to shape technical architecture while focusing on team growth.
• Translate research into production-ready ML systems at Xero’s scale.

Skills

Team leadership

System architecture

MLOps

Production code

Roadmapping

Cross-functional collaboration

Hands-on coding

Job description

The role & impact

As Engineering Manager, you'll lead a high-performing, inclusive team of ML and platform engineers, responsible for turning research and models into production-grade ML infrastructure that runs at scale. You'll balance predictable delivery with a relentless focus on system health, taking the lead on MLOps maturity, observability and the operational realities of running AI systems in production, not just shipping features.

You'll work closely with senior individual contributors, including staff and principal engineers, who shape the technical architecture, while you focus on team growth, delivery rigour and championing AI-native workflows across the software development lifecycle. This is a role for someone who wants to build both the systems and the people capable of running AI at Xero's scale.

The team & how they connect

You'll join Xero's Data & AI organisation, within the AI Products division, reporting to the GM of ML Engineering. The team takes research and models from applied scientists and turns them into safe, scalable, production ML infrastructure that millions of small businesses rely on every day. It's a close, cross-functional group that partners tightly with Applied Science, Data Engineering and Product to move ideas from prototype to production.

The team is currently working on

• Production ML infrastructure and MLOps supporting Xero's AI-native product suite
• Systems powering JAX (Just Ask Xero), Xero's conversational GenAI assistant
• Auto Bank Rec and smart document capture, including AI-powered transaction matching and extraction from bills and receipts
• Future work extending cash flow forecasting and other embedded ML capabilities

Where and how you can work

We support a flexible working model that empowers you to balance your professional and personal life. You will have the option to work in a hybrid capacity, combining the focus of remote work with the collaboration of our office spaces and team boost days to foster connection and alignment.

Here are some of the things we're looking for, for this role

• You’ll bring a strong track record of leading, coaching and developing engineers, with real experience building team capability over time
• Strong technical judgement across architecture, system design and engineering trade-offs is part of how you naturally operate
• You’ve translated business goals into executable engineering plans, balancing short-term delivery against long-term system health
• Delivering roadmaps predictably in fast-paced, evolving environments is familiar territory for you
• You stay hands-on when it counts, comfortable writing, reviewing or debugging production code to unblock your team or support an incident
• Experience with machine learning or AI systems is a bonus rather than a requirement, though genuine curiosity about the space will help you thrive here