2 days ago

Engineering Manager - ML & AI

Xero

Melbourne

2 days ago

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Engineering Manager needed for Xero's Machine Learning & AI team in Melbourne. Lead a team of ML and platform engineers to build production-grade ML infrastructure at scale, with a focus on MLOps maturity, observability, and team growth.

AI-enabledHybridFull-timeEntry LevelMachine LearningMLOps

Engineering Manager - ML & AI

Apply

AI-enabledHybridFull-timeEntry LevelMachine Learning

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

Hybrid: option to work remotely with collaboration in office spaces and team boost days

Employment Type

Full-time

Experience Level

Not Applicable

Core Qualifications

Technical (Must-have)

Machine LearningMLOpsProduction ML infrastructureSystem designArchitectureObservabilityAI systemsSoftware development lifecycleAI-native workflows

Soft Skills

LeadershipCoachingDeveloping engineersTechnical judgementCommunicationTeam growthDelivery rigourCuriosity

Key Responsibilities

•Lead a high-performing, inclusive team of ML and platform engineers
•Turn research and models into production-grade ML infrastructure that runs at scale
•Balance predictable delivery with a relentless focus on system health
•Take the lead on MLOps maturity, observability and operational realities of running AI systems in production
•Work closely with senior individual contributors, including staff and principal engineers
•Focus on team growth, delivery rigour and championing AI-native workflows across the software development lifecycle
•Translate business goals into executable engineering plans
•Deliver roadmaps predictably in fast-paced, evolving environments
•Stay hands-on when it counts, comfortable writing, reviewing or debugging production code to unblock your team or support an incident

Engineering ManagerMachine LearningAIMLOpsSoftware DevelopmentFull-timeHybridMelbourneXero

Key Responsibilities

•Lead a high-performing, inclusive team of ML and platform engineers

•Turn research and models into production-grade ML infrastructure that runs at scale

•Balance predictable delivery with a relentless focus on system health

•Take the lead on MLOps maturity, observability and operational realities of running AI systems in production

•Work closely with senior individual contributors, including staff and principal engineers

•Focus on team growth, delivery rigour and championing AI-native workflows across the software development lifecycle

•Translate business goals into executable engineering plans

•Deliver roadmaps predictably in fast-paced, evolving environments

•Stay hands-on when it counts, comfortable writing, reviewing or debugging production code to unblock your team or support an incident