Top AI & Machine Learning Jobs in Melbourne, VIC

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.

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.

Develop and deploy machine learning products and services by translating complex business problems into applied research programs. Build robust data pipelines and reusable code in Python or Scala, develop and evaluate applications involving NLP, computer vision, and LLMs, and monitor algorithms at scale. The role also involves managing research risks, collaborating cross-functionally, mentoring colleagues, communicating complex findings, and raising scientific and engineering standards.

Leads ML and platform engineering teams building scalable, production-grade machine learning infrastructure. Responsibilities include coaching engineers, guiding architecture and engineering trade-offs, improving MLOps maturity and observability, translating business goals into delivery plans, managing roadmaps, maintaining system health, and supporting production incidents through code review, debugging, or hands-on development. The role supports AI products including conversational GenAI, automated bank reconciliation, document capture, and cash-flow forecasting.

As a Senior Engineer, you'll develop AI workflows, lead delivery of AI agents, and integrate systems while collaborating with business teams to automate processes.

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

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.