Top Machine Learning Jobs in Melbourne

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.

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.

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.

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.

Conduct quantitative equity research by developing and testing systematic investment strategies, building reproducible datasets and analytical workflows, and evaluating portfolio risks, costs, capacity, and implementation constraints. The role requires rigorous statistical and econometric analysis, bias and overfitting controls, collaboration with portfolio managers, and clear communication of research findings to investment and governance stakeholders.

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.

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.

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.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead and mentor engineers to design, build, and maintain scalable automation and onboarding systems. Implement AI/data-driven risk detection, collaborate with product/ops/compliance, drive architecture and reliability, and improve operational efficiency.

Design, build, validate, deploy, and monitor machine learning, GenAI, and Agentic AI solutions for customer service and branch experiences. Translate business challenges into practical AI approaches, productionise models, and help shape data strategy, governance, quality, risk, and responsible AI practices. Partner with product, engineering, risk, and business stakeholders while communicating technical recommendations to senior leaders.

Develop machine learning and deep learning models that digitize electricity infrastructure from LiDAR, imagery, GIS, and vector data. Build scalable training, serving, QA, and monitoring pipelines; identify data and distribution drift; improve model deployment speed; and apply MLOps best practices. The role also involves defining ML strategies, solving ambiguous problems, collaborating asynchronously across functions, and mentoring junior engineers.

Design, model, optimise, prototype, align, and characterise optical and electro-optical imaging systems for autonomous sensing platforms. Responsibilities include optical tolerancing, sensitivity analysis, stray-light and ghost analysis, component selection, optomechanical integration, image-quality optimisation, manufacturing transition, and collaboration with machine-learning and computer-vision teams. The role requires hands-on optical engineering experience and eligibility for an Australian Defence security clearance.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Own end-to-end implementation delivery for multiple enterprise utility customers, from contract signature through go-live, hypercare, and handover to Customer Success. Coordinate cross-functional internal teams and customer stakeholders, manage scope, milestones, dependencies, risks, resources, and change, and provide executive updates. Improve implementation processes, tooling, templates, and playbooks while reducing time to customer value.

Manages science and technology initiatives for sustainable industrial fisheries. Leads multidisciplinary projects involving electronic monitoring, AI, machine learning, computer vision, data systems, hardware, software deployment, and seafood traceability. Oversees vendors, contractors, budgets, procurements, technical assessments, and deployment of systems in remote environments. Translates technical concepts for diverse stakeholders and supports technology evaluation, scaling, and conservation strategy. The role may require domestic and international travel of 25% or more.

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.

Lead ClickUp’s APJ revenue strategy, initially focusing on building direct commercial and enterprise sales across Australia and New Zealand while supporting channel-led growth across Southeast Asia. Responsibilities include recruiting and developing sales teams, managing enterprise accounts, driving partner collaboration, forecasting pipeline, owning ARR targets, developing territory strategies, and engaging senior customer and partner executives.

Owns high-impact ANZ strategic projects spanning commercial growth, customer expansion, partnerships, sales, and delivery. The role translates regional priorities into workstreams, milestones, dashboards, operating cadences, and repeatable playbooks. It resolves cross-functional blockers, improves sales-to-delivery execution, supports priority accounts and partners, and uses data, automation, and AI to improve regional performance. Success is measured through revenue retention, opportunity progression, partner contribution, delivery efficiency, and stronger operating leverage for regional leadership.

Own the end-to-end finance and accounting function across entities and jurisdictions. Responsibilities include financial close, consolidations, reporting, cash management, compliance, tax, audits, external partners, finance systems, controls, automation, budgeting, forecasting, fundraising support, and team leadership. The role will build scalable finance infrastructure, improve operational processes, provide decision support to executives and investors, and ensure accurate, timely, and reliable financial information.

Manage and grow Federal government accounts for NVIDIA Sovereign AI and Data Center products. Drive demand creation, co-selling, forecasting, strategic partnerships, go-to-market programs, revenue growth, and product adoption across agencies, OEMs, partners, and research organizations. Serve as the primary customer relationship owner while translating customer needs into future product strategy. Engage executives, program managers, engineers, and public-sector stakeholders.

Build, evaluate, and ship machine-learning ranking and relevance systems for private, team, and brand content search. Improve relevance, recall, result understanding, search recovery, and exact-title matching. Contribute to Elasticsearch, OpenSearch, vector search, indexing, annotation, and agent-driven search. Investigate production regressions, run experiments, measure search-quality impact, own scoped projects end to end, and communicate technical trade-offs across technical and nontechnical stakeholders.

Hunts for new-logo enterprise sales opportunities for IFS AI solutions, builds strategic-account pipeline, leads consultative executive discussions, and closes complex $500,000+ ACV deals. The role requires positioning AI value, partnering with regional and solution teams, engaging multiple stakeholders, and applying industry expertise across sectors such as manufacturing, aerospace, energy, construction, and telecom.

Build and deliver production-grade generative AI and LLM applications using Python, machine learning, cloud platforms, APIs, SQL, and data pipelines. Contribute to AI evaluation, observability, testing, code reviews, and technical standards. Collaborate with clients and domain experts to define solutions, explain technical concepts, and communicate risks and trade-offs. The role supports end-to-end AI/ML delivery while developing technical and consultative expertise.

Develop and deploy credit risk, IFRS 9 provisioning, forecasting, stress-testing, and emerging-risk models. Responsibilities include sourcing and validating data, researching explainable modeling techniques, monitoring model performance, maintaining governance standards, conducting ad hoc analytics, and communicating insights to business stakeholders. The role requires collaboration, commercial acumen, timely delivery, and familiarity with traditional statistical models, machine learning, AI tools, and the consumer credit lifecycle.