Top Machine Learning Jobs in Melbourne

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Deploys, configures, optimizes, and supports CrowdStrike Falcon’s AI-powered detection and response capabilities. The role develops automation playbooks and API integrations, tunes machine-learning detection models, troubleshoots customer issues, supports threat hunting and forensic analysis, integrates security workflows, and leads AI workshops. It also requires customer engagement, technical presentations, project prioritization, cross-functional leadership, and mentoring while applying cybersecurity, AI/ML, and platform deployment expertise.

Lead end-to-end engineering talent acquisition across APJ, translating business priorities into hiring strategies and personally delivering complex senior engineering searches. Build and manage an in-house recruiting team, oversee RPO and external partners, develop specialized technical talent pipelines, improve assessment and candidate experience, and use recruiting analytics to guide decisions. The role requires navigating confidential, security-sensitive hiring environments and partnering closely with engineering leaders across autonomy, robotics, simulation, machine learning, and infrastructure.

Design, build, and operate production systems powering Xero’s ML, LLM, and agentic AI features. Responsibilities include backend services, orchestration, infrastructure, APIs, cloud deployment, observability, reliability, cost and latency optimization, incident response, and large-scale data processing. The role also involves establishing engineering standards, mentoring engineers, and partnering with scientists and cross-functional teams to deliver dependable AI products.

Design, build, and operate production backend systems, services, pipelines, and infrastructure powering Xero’s LLM-driven and agentic AI products. Responsibilities include system architecture, APIs, cloud infrastructure, reliability, observability, cost and latency management, on-call support, incident response, data processing, and mentoring engineers. The role partners with scientists and cross-functional teams to operationalize AI systems at scale without requiring model-training expertise.

Serves as the primary technical and customer support contact for complex Azure issues and projects. Troubleshoots mission-critical applications, conducts health checks and root-cause analysis, manages escalations, advises on Azure configurations and deployments, and communicates technical issues to varied audiences. Synthesizes customer feedback, identifies product and knowledge gaps, advocates for platform improvements, and partners with engineering, product, field, and support teams to improve reliability and customer experience.

Lead development of credit risk stress-testing models across retail and non-retail portfolios. Apply statistical, econometric, predictive, and macroeconomic modeling to assess portfolio vulnerabilities, credit losses, and risk resilience under changing economic conditions. Use R, Python, SQL, Teradata, and AWS to analyze complex data, explain model assumptions and limitations, support validation and planning, guide analysts, and provide actionable insights to risk and business stakeholders.

Lead engineering for MYOB’s customer-facing agentic AI products from discovery through production. Set technical direction, architecture, evaluation standards, and responsible AI practices while balancing quality, latency, reliability, safety, and cost. Lead multidisciplinary teams across AI engineering, data science, and software engineering; develop senior technical leaders; and partner with Product, Design, Security, Architecture, and external AI providers to deliver scalable AI experiences.

Design, develop, deploy, and support AI-powered applications and services using generative AI, machine learning, cloud platforms, APIs, and data pipelines. Translate business needs into scalable architectures, optimize AI systems, troubleshoot performance issues, and incorporate security, privacy, governance, and responsible AI practices. Collaborate with technical and business stakeholders, evaluate emerging technologies, contribute to standards, and mentor team members.

Provides technical leadership for secure, resilient cloud-native content platforms. Responsibilities include designing and operating AWS and Kubernetes solutions, developing software and automation, integrating AI capabilities, improving CI/CD and infrastructure-as-code practices, embedding testing and observability, strengthening reliability and disaster recovery, and mentoring engineers. The role supports the migration and ongoing enhancement of OpenText document retention and archiving services.

Leads the creation of dentsu’s AI Engineering discipline and portfolio of AI-driven marketing products. Responsibilities include defining architecture, engineering standards, evaluation practices, guardrails, and technical strategy; designing and deploying production-ready AI systems, LLM applications, agentic workflows, and intelligent automation; influencing stakeholders; and shaping long-term AI platform capabilities and strategy.

Design and deliver scalable data pipelines, models, and reusable solutions using Microsoft Fabric and Azure technologies. Apply AI, automation, DevOps, security, privacy, governance, and data-quality practices to large enterprise datasets. Partner with stakeholders to define requirements, manage delivery risks, and produce reliable data solutions. Provide technical leadership, coach engineers, and advance data engineering standards and practices.

Leads the development and deployment of commercial AI products, platforms, LLM applications, agentic workflows, and intelligent automation. Establishes AI engineering standards, architecture patterns, evaluation practices, and guardrails across dentsu. Shapes the AI engineering function, guides technical strategy, manages complex initiatives from strategy through optimization, and translates emerging technologies into scalable business outcomes.

Leads a data engineering chapter supporting financial crime platforms. The role combines hands-on solution design, architecture, coding, technical reviews, troubleshooting, and delivery of trusted data products with people leadership, coaching, standards, and capability development. Responsibilities include modernizing data platforms using AWS and Snowflake, strengthening data quality and controls, advancing DevSecOps and observability, and partnering across risk, compliance, technology, governance, and business teams.

Lead enterprise AI discovery and validation across Australia, identifying high-value private AI use cases and assessing their technical feasibility, data readiness, governance, security, and production potential. Facilitate technical workshops with customers and ecosystem partners, develop proofs of concept and reference architectures, define engineering requirements, and hand qualified opportunities to forward-deployed engineering teams. Build reusable AI patterns, establish regional domain expertise, and communicate effectively with technical and executive stakeholders.

Drive go-to-market execution and quota attainment for Fortinet’s AI security solutions across Australia. Build pipeline with account teams and partners, lead enterprise discovery, presentations, workshops, demonstrations, and strategic selling, and close complex multi-stakeholder deals. Advise customers on AI governance, runtime protection, guardrails, red-teaming, and shadow AI. Train sales teams and channel partners while engaging C-level security and AI decision-makers.

Leads pre-sales technical execution for AI security solutions across Australia. Responsibilities include technical discovery, architecture reviews, demonstrations, proof-of-concept support, customer deployments, partner enablement, sales engineer training, demo environment management, and post-sales handoffs. Builds relationships with technical decision-makers, supports cloud and self-hosted AI deployments, and evangelizes secure agentic AI practices.

Leads the ML organization developing Zendesk Custom Agents, setting strategy, managing and mentoring engineers and scientists, and delivering production generative AI systems. Oversees agent reasoning, tool use, knowledge grounding, evaluation, reliability, safety, latency, and cost optimization. Partners across Product, Engineering, Design, Security, and Science to establish quality standards, monitor production performance, and connect ML improvements to measurable customer outcomes.

Provides technical leadership for geometallurgical programs across mining exploration, studies, and project development. Develops frameworks, oversees ore characterization and testwork, integrates geological, mineralogical, metallurgical, and operational datasets, and builds predictive models supporting resource evaluation, mine planning, process design, and investment decisions. The role identifies technical risks, reviews study outcomes, collaborates across disciplines, and mentors geometallurgists and mineralogists.

Manage investment cost ratio calculations and regulatory reporting across investment options. Apply ASIC Regulatory Guide 97, support regulatory change, improve reporting controls, coordinate insurance renewals, and own CPS 190 Recovery and Exit Planning activities. Use Power BI and analytics to identify inconsistencies, provide insights, and explore automation and AI solutions. Engage stakeholders, auditors, regulators, brokers, advisers, and governance committees.

Provides enterprise-wide security engineering and architecture leadership across corporate and technology functions. Leads complex security programs, architecture reviews, threat assessments, control design, and risk-based decisions. Advises senior executives, defines scalable security patterns and guardrails, mentors security practitioners, and guides capabilities including EDR, SIEM, observability, AI security, DevSecOps, and cloud security.

Designs, develops, deploys, and supports secure AI, machine learning, generative AI, copilots, agents, and intelligent automation solutions. Responsibilities include building RAG systems, APIs, integrations, MLOps and DevOps practices, monitoring, governance, and responsible AI controls. The role translates business needs into production-ready AI products and partners with data engineering, cybersecurity, privacy, risk, and governance teams.

Lead product vision, strategy, and execution for Walmart’s Enterprise AI Platform. Define durable platform capabilities spanning MLOps, model access, inference, developer experience, governance, reliability, observability, and AI economics. Partner with engineering, infrastructure, security, governance, finance, and business teams to drive adoption, safety, cost efficiency, and enterprise-scale outcomes. Build and develop a high-performing product organization while translating rapidly evolving AI technologies into durable platform strategy and execution.

Conduct research and experimentation in computer vision, generative AI, multimodal models, and machine learning for AI-powered video creation. Develop, adapt, evaluate, and improve models and datasets for production use, create evaluation tools, analyze model quality, and collaborate with engineering and product teams to ship scalable experiences. Document findings, monitor training or production tasks, and apply responsible AI, privacy, and security principles.

Build and improve core AI and machine learning infrastructure enabling teams to develop, train, evaluate, deploy, and operate ML models and pipelines. Design scalable distributed systems, support end-to-end ML lifecycles, implement MLOps automation, collaborate with product engineering teams, lead projects from design through launch, review code, document solutions, and mentor junior engineers. Work may include dataset curation, LLM fine-tuning, model serving, monitoring, and generative AI systems.