Top Machine Learning Jobs in Melbourne

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 a data science chapter developing and scaling machine learning, Generative AI, and agentic AI solutions for fraud and scams. Guide technical direction, architecture, experimentation, evaluation, deployment, monitoring, responsible AI, and model governance. Partner with product, engineering, MLOps, risk, and business teams while coaching data scientists and strengthening delivery practices in a regulated environment.

Develop and deploy AI, generative AI, machine learning, and data science solutions for consulting clients. Responsibilities include building RAG pipelines, AI agents, LLM applications, predictive models, deep learning systems, optimization solutions, and big-data applications. The role also involves model evaluation, AI observability, governance, research, and collaboration with architects, engineers, data scientists, and clients.

Design, build, validate, deploy, and monitor machine learning, Generative AI, and Agentic AI solutions for branch and customer service experiences. Translate business challenges into AI approaches, guide data governance and risk considerations, shape data science strategy, and partner with product, engineering, risk, and business stakeholders. Help move AI solutions from experimentation into reliable production while communicating technical recommendations to senior stakeholders.

Lead a specialist data science chapter delivering production-grade machine learning, generative AI, and agentic AI solutions. Drive architecture, experimentation, evaluation, deployment, monitoring, responsible AI, and model governance in a regulated environment. Partner with engineering, MLOps, risk, architecture, and business teams while coaching data scientists and advancing technical excellence across fraud and scam prevention initiatives.

Lead the creation of REA’s residential machine learning capability, managing a distributed team across multiple product tribes. Set technical direction, engineering standards, delivery practices, and team ways of working. Provide guidance on ML, data science, data engineering, architecture, evaluation, governance, and operational excellence. Partner with product, design, and AI teams to deliver pragmatic AI/ML products using NLP, LLMs, semantic search, and related technologies.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

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.

Lead frontier research in on-device AI, focusing on inference efficiency, model routing, autonomous research pipelines, and real-world evaluations. The role owns significant parts of the research agenda and requires designing experiments, deriving actionable insights, and translating novel ideas into performance improvements. Work may include speculative decoding, quantization, distillation, reinforcement learning, accelerator optimization, and deployment under strict device constraints.

Graduate software engineers will contribute across any layer of Maincode’s production AI system, Matilda. Responsibilities may include supporting model training, inference, and infrastructure. Candidates should research the company and explain how they can contribute to building world-class Australian-made AI. Previous industry experience, including internships, is advantageous but not required.

Lead the evolution of customer onboarding and data conversion through AI, automation, AWS cloud services, and data intelligence. The role designs scalable automation, improves data quality, optimizes platforms and pipelines, develops predictive capabilities, and partners cross-functionally to reduce migration risk and improve customer outcomes.

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.

Develop geophysical modeling capabilities for quantum sensing applications, including forward propagation models, potential field inversion, anomaly detection, simulation analysis, statistical assessment, and machine learning algorithms. The role involves building software for field data analysis, modeling underground systems, validating sensor applications, and optimizing surveying techniques in collaboration with geophysics and technology teams.

Design and implement novel real-time computer vision algorithms in C++, develop and maintain C++ CV pipelines for autonomous systems, integrate deep learning models with perception systems, collaborate with AI/robotics/optical engineers, and keep up with academic research to deliver reliable edge solutions.

Lead end-to-end Marketing Technology projects and retainers, managing scopes, timelines, budgets, risks, client relationships, and cross-functional delivery. Translate business goals into actionable plans and value-driven solutions involving MarTech, data analytics, decisioning, marketing automation, and consultancy. Use AI to improve project management efficiency, support change management and training, and contribute to organizational growth and continuous improvement.

Leads the engineering function for SEEK’s AI and platform services, managing and developing engineers while setting technical direction. Responsibilities include designing scalable backend systems and APIs, productionizing machine learning models, improving DevOps practices, shaping product and technical priorities, managing integrations, and partnering with data scientists, product managers, and business stakeholders to deliver reliable AI-enabled capabilities.

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.

Serve as a Middle East and North Africa subject matter expert, monitoring real-time data feeds, analyzing geopolitical events, and improving AI models, tooling, and alert quality. The role involves making decisions with incomplete information, leading strategic initiatives, supporting regional training, and collaborating across teams. Fluency in Arabic and English is required, with additional regional languages beneficial. Work is hybrid or remote in Australia, with core hours and rotational weekend commitments.

Lead end-to-end data science initiatives involving exploratory analysis, behavioral insights, experimentation, recommendation, ranking, personalization, and machine learning. Develop and productionize models, design offline and online evaluations, measure commercial impact, and establish rigorous modeling and documentation standards. Collaborate with Product, Data, Technology, and Commercial teams to solve complex customer problems and improve vehicle marketplace experiences.

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.

Serve as a trusted technical advisor for AI and cloud infrastructure solutions. Partner with customers, sales teams, and technology partners to understand business needs, design architectures, lead workshops and technical evaluations, and deliver demonstrations. Translate AI strategies into scalable, secure deployments using modern infrastructure, cloud, networking, storage, GPU, container, and model-serving technologies. Present solutions to technical and executive audiences while supporting customer adoption and measurable outcomes.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Leads UniSuper’s data science capability and a specialist team delivering predictive models, machine learning, and advanced analytics. Sets technical standards, oversees model development and production maintenance, establishes governance, monitors performance and fairness, and translates analytical outputs into business decisions. Partners with senior stakeholders and Product Owners to identify valuable opportunities, prioritizes work, evaluates commercial and operational impact, and supports financial crime, efficiency, and regulatory objectives.

Leads commercial performance for global and regional eCommerce markets, improving revenue, margin, EBIT, conversion, AOV, demand shaping, customer experience, and retention. Partners with market leaders to diagnose performance, recommend actions, align improvement plans, govern KPI frameworks, and ensure adoption. Leads data storytellers, establishes analytical and measurement standards, develops global playbooks, and influences senior stakeholders across a global matrix organization.

Design, implement, and integrate real-time computer vision algorithms into high-performance C++ pipelines for edge-deployed autonomous systems. Focus on custom perception capabilities, model adaptation and evaluation, inference optimization, dataset curation, and cross-functional integration with AI, robotics, and optical teams to deliver robust mission-capable solutions.