Top Tech Jobs & Startup Jobs in Melbourne

Lead design and implementation of AI research and production infrastructure. Mentor junior engineers, and collaborate to enhance data usability, solving complex technical challenges.

Lead multiple product teams to define and execute strategy for identity, authentication/authorization, fraud detection and core platform reliability. Set vision for scaling Xero to larger businesses, drive cross-functional alignment, coach product managers, manage roadmaps, launch and iterate products, and ensure security, compliance and commercial outcomes.

Design, build, and operate scalable data platform pipelines and models using Snowflake, dbt, Prefect, Flink and AWS. Own end-to-end platform components, apply IaC with Terraform, implement CI/CD and testing, manage production systems, monitoring and incident response, and improve data quality and self-service capabilities for downstream teams.

Lead end-to-end mobile product strategy and roadmaps, run customer research, define success metrics and OKRs, manage lifecycle and compliance, and deliver AI/automation features that improve customer and commercial outcomes while coaching teammates.

Design, build, and operate an internal AI skills marketplace and distribution platform. Own end-to-end delivery from experimentation to production, create runbooks, monitoring, rollback plans, and provide guidance on authoring agent skills while collaborating with vendors and engineering teams.

Lead design and implementation of large-scale distributed production systems powering generative AI features. Own architecture, mentor engineers, manage technical debt, operationalize ML/LLM models, and collaborate with scientists to productionize research using Python, SQL, Spark/Dask, AWS, and Kubernetes.

Lead Xero's shared Data Engineering function to execute data and platform strategy, deliver reliable self-service and AI-first data platforms, embed governance-by-design, improve observability and operational health, grow senior leadership, and enable domain teams through reusable platforms, patterns and guardrails.

Lead end-to-end data science projects for Payroll: frame problems, build experiments, productionise ML models, measure AI product outcomes, and mentor junior scientists. Work cross-functionally with product, engineering and design to improve conversion, retention and product outcomes using modern data stacks and AI automation.

Lead end-to-end data science roadmaps for a product domain: define metrics, drive experimentation, productionise decision models, shape AI-driven analytics, build reusable assets, mentor data scientists, and advise senior stakeholders.

Build internal tooling and platform improvements for database reliability. Split time between software engineering and database operations: monitoring, observability, performance tuning, incident response, migration to RDS/Postgres, and automating fleet management to improve resilience and developer efficiency.