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.

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.

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.

Own Linktree’s production AI platform, including pipelines, model and prompt versioning, evaluations, monitoring, rollback, cost, latency, and reliability. Drive internal AI adoption through tooling, agent workflows, and enablement across Product, Engineering, Marketing, and Support. Establish practical governance for data handling and vendor risk, make build-versus-buy decisions, align AI investment with business priorities, and mentor engineers and analysts.

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.

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.

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.

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.

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.

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.

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.

Customer-facing Threat Analyst supporting financial institutions with fraud detection, prevention strategy, behavioral biometrics, and advanced analytics. Responsibilities include advising customers, deploying fraud rules, measuring detection performance, researching emerging threats, delivering reporting and insights, educating prospects, supporting sales, and collaborating with Data Science and product teams. The role requires presenting to varied audiences, building customer relationships, and traveling across Australia and New Zealand for customer meetings and workshops.

Provides strategic cybersecurity advisory and technical leadership across enterprise security assurance engagements. Leads security assessments, risk reviews, governance improvements, and security-by-design initiatives; develops standards, procedures, guidance, templates, and playbooks; advises technical and business stakeholders on controls and risk treatment; promotes security awareness; and uses metrics to improve cybersecurity processes and stakeholder outcomes.

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.

Develop threat models, statistical verification algorithms, calibrated tests, and production verification services for decentralized training and inference on untrusted heterogeneous hardware. The role focuses on detecting incorrect model outputs, malicious training behavior, free-riding, poisoning, data extraction, and reward manipulation while controlling false positives and negatives. Candidates need deep statistics and probability expertise, experience shipping or publishing calibrated decision systems, and familiarity with statistical testing, re-execution, cryptographic proofs, and trusted hardware.