Top Machine Learning Jobs in Melbourne

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.

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.

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.

Design, build, deploy, and operate agentic LLM harnesses powering Rovo AI features. Improve production model quality, reliability, and cost; develop evaluations, datasets, benchmarks, fine-tuning, and model-serving capabilities. Shape long-term LLM engineering direction, lead technical initiatives, influence stakeholders, guide virtual squads, and coach junior engineers.

Leads hands-on development of AI-enabled applications, automations, and data solutions for investment and finance teams. Embeds with portfolio managers, traders, researchers, and finance professionals to identify opportunities, modernize spreadsheet and EUCT workflows, and deliver governed production capabilities. Establishes engineering and AI evaluation patterns, guides technical direction, mentors engineers, and partners with security, risk, architecture, and platform teams. Requires strong software and data engineering expertise, Python proficiency, cloud AI and data platform experience, and financial-services domain knowledge.

Leads Xero’s global data science, analytics, ML, and AI strategy across multiple product areas. The role manages and scales a multidisciplinary organization, advises executive leadership, oversees AI governance, model risk, data quality, privacy, experimentation, and enterprise data architecture, and drives AI-powered products including chatbots and agentic workflows. It also establishes operating models, develops leaders, manages strategic risks, and partners cross-functionally to deliver company-wide data and AI impact.

Lead product strategy and roadmaps for AI Enrichment and Listings Enrichment squads. Own audience modelling and segmentation vision, prioritize data and ML capabilities, build investment cases, and balance reusable platform development with near-term outcomes. Partner with product, engineering, and data science teams on model lifecycles, experimentation, evaluation, monitoring, and adoption. Champion data quality, privacy, responsible AI, and measurable business impact across REA’s consumer and customer experiences.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Unpaid, part-time internship offering hands-on mentorship in robot learning and imitation learning. Work ranges from setting up robot simulation frameworks, robot integration and human demonstration data collection, to designing and training ML algorithms and deploying models on robots. Remote theoretical work allowed; Sydney-based preferred for hardware testing.

Own the vision, strategy, roadmap, and delivery of enterprise identity graph and security signal products. Translate identity, data, AI, and security needs into measurable outcomes, prioritize capabilities, lead cross-functional engineering delivery, manage dependencies and risks, and communicate progress to senior stakeholders. The role focuses on connected identity data, graph analytics, real-time risk signals, and automated security outcomes.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Drive revenue through complex enterprise sales by designing cloud data security solutions, conducting proof-of-value engagements and technical demonstrations, consulting with executive stakeholders, supporting partners, and coordinating internal resources. The role requires expertise in cloud security, data protection, IAM, networking, databases, encryption, privacy, and regulatory frameworks, along with strong presentation and problem-solving skills. Travel up to 50% within the assigned territory is required.

Lead customer deployments of AI engineering systems, translating physical engineering challenges into AI use cases. Design integrations with customer data sources and tools, fine-tune and evaluate models using production data, collaborate with engineering and AI teams, and troubleshoot live deployments. The role requires direct customer engagement, rapid prototyping, production delivery, incident response, and frequent customer-site work in Perth.

Sessional economics tutors and markers will teach or assess undergraduate and postgraduate economics and econometrics courses. Applicants may contribute expertise in areas such as microeconomics, behavioural economics, game theory, digital economics, time series analysis, big data, machine learning, and blockchain. Successful applicants will engage students positively, support learning, and may be considered for future casual research opportunities. Work is casual and dependent on course demand and timetabling.

Casual sessional teaching and marking opportunities in finance at RMIT’s School of Economics, Finance and Marketing. Successful applicants may teach undergraduate or postgraduate subjects including risk management, sustainable finance, portfolio management, financial modelling, fintech, quantitative finance, trading, and machine learning in finance. Responsibilities include engaging students, facilitating learning in classrooms or the RMIT Trading Facility, and potentially contributing to marking or future research activities.

Develop, deploy, and maintain data science products for Shell Energy. Build machine learning and advanced analytics models for customer churn, load behavior, forecasting, demand response, and energy optimization. Collaborate with stakeholders, engineers, analysts, and technology teams to deliver scalable solutions. Apply DevOps principles, monitor model performance, troubleshoot technical issues, and communicate actionable insights to technical and business audiences.

Manage and grow strategic enterprise accounts through revenue retention, expansion, consultative selling, and customer relationship management. Develop account plans, meet revenue targets, coordinate reseller and internal stakeholders, oversee customer activities across regions, support forecasting and business reviews, identify product expansion opportunities, and provide customer insights to sales, product, and marketing teams. The role requires strong communication, negotiation, analytical, project management, and technical sales skills, with travel up to 50% internationally.