Top Machine Learning Jobs in Melbourne

Six-month graduate program supporting Falcon Complete security operations. Responsibilities include triaging issues, escalating potential incidents, securely implementing customer-environment changes, communicating with customers, coordinating incident response, and troubleshooting technical issues. Participants receive mentoring, hands-on training, and exposure to threat detection, investigation, containment, remediation, and CrowdStrike Falcon administration.

Participate in a six-month Falcon Complete graduate program supporting cybersecurity operations. Responsibilities include triaging issues, escalating potential incidents, implementing secure customer-environment changes, communicating with customers, coordinating incident response, and troubleshooting technical issues. The role provides training in Falcon platform administration, threat detection, investigation, containment, and remediation, with opportunities to earn CrowdStrike certifications and potentially transition to a Falcon Complete Associate Analyst position.

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.

Lead and expand a strategic enterprise financial-services account for FICO. Build C-suite relationships, develop joint business plans, structure and negotiate multimillion-dollar SaaS deals, collaborate on tailored analytics and decisioning solutions, advocate for client needs, and achieve growth and profitability targets. The role requires deep banking and risk-industry knowledge, consultative enterprise sales expertise, and travel of up to 50%.

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.

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.

Leads Vocus’s AI Science team and owns predictive modeling, optimization, experimentation, and generative AI evaluation. The role ensures evidence-based model selection, data quality, measurable business value, and benefits validation. It also supports executives as an AI Chief of Staff by improving AI proficiency and adoption. Strong applied ML, statistics, experimentation, Python, SQL, leadership, and communication skills are required.

Leads Vocus’s AI Technical Solutions team, building in-house AI engineering and science capabilities and delivering secure, evaluated, reusable, production-grade AI solutions. Responsibilities include designing agentic platforms and enterprise AI architecture, establishing engineering and AI control standards, managing reusable components, developing talent pipelines, and delivering measurable commercial value. Requires deep software or ML engineering expertise, enterprise-scale LLM or ML production experience, AWS knowledge, and understanding of security, privacy, and controls for critical infrastructure.

Lead technical pre-sales for enterprise retail and telecommunications customers, presenting Snowflake’s AI Data Cloud, designing solutions, delivering demos and proofs of concept, and supporting deal closure. Partner with account executives, channel partners, Product, Engineering, and Marketing. Translate customer architectures and business needs into Snowflake solutions involving data platforms, analytics, AI, customer 360, forecasting, personalization, and secure data sharing.

Leads discovery and validation of new marketplace business opportunities, using experiments, data, AI, and rapid prototyping to identify scalable products. Balances buyer and seller needs, builds partnerships, develops product vision and strategy, and secures senior stakeholder support and funding. Requires zero-to-one product experience, strong product judgment, persuasive communication, and cross-functional influence.

Design, build, and deploy production-grade software, data, machine learning, and Generative AI solutions for enterprise clients. Integrate data platforms, communicate technical solutions to stakeholders, ensure delivery quality, contribute to engineering processes, and mentor less-experienced engineers. The role is hands-on and offers growth into technical leadership.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Design and maintain data science models and software that simulate and predict electricity grid behavior and assess new connection impacts. Build data pipelines, perform statistical analysis, deploy machine learning models, and collaborate with engineering and product teams. Engage customers to align solutions with requirements, establish development and monitoring practices, and contribute to data science architecture and strategy.

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 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.

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.

Own the strategy, roadmap, development lifecycle, and go-to-market activities for fraud analytics products. Lead customer discovery, prioritize product improvements, analyze complex data, collaborate with development and data science teams, and use market and customer insights to improve user outcomes. The role requires fraud prevention expertise, statistical modeling and AI/machine learning knowledge, strong stakeholder communication, and flexible UK-US working hours.