Top Machine Learning Jobs in Melbourne

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.

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.

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.

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.

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.

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.

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.

Build and optimize an on-device AI inference stack, including custom inference engines, GPU kernels, serving runtimes, model routing, continuous batching, and decoding strategies. Develop C++ systems for low-latency inference across Apple, NVIDIA, AMD, Snapdragon, and other platforms. Profile performance, optimize memory and compute bottlenecks, and translate ML research into production infrastructure.

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.

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.

Design, build, test, deploy, and monitor enterprise AI/ML solutions and agentic workflows across Azure and Databricks. Develop production-quality Python, C#, and SQL code, MLOps pipelines, model evaluation and monitoring systems, and governed AI platforms. Collaborate with architecture, security, governance, platform, product, and business teams while contributing to documentation, engineering standards, code reviews, responsible AI, FinOps, and mentoring junior engineers.

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.

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.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Develop and improve machine learning models for clinical AI products, from data curation and experimentation through validation, deployment, and production monitoring. Partner with clinicians, software engineers, product teams, and quality colleagues to evaluate robustness, optimize inference, document results, and support regulated product delivery. Mentor colleagues and make evidence-based technical decisions.

Design, implement, and maintain Abi’s production autonomy stack, including path planning, SLAM, collision avoidance, perception integration, simulation, and safety mechanisms. Harden software and integrated hardware systems through testing and validation, support a deployed robot fleet, debug sensors and effectors, improve deployment processes and user experience, and contribute to systems engineering across the product lifecycle. Lead cross-functional technical discussions and perform customer site visits for testing and feature validation.

Lead global regulatory strategies and submissions for orthopedic medical devices and SaMD across US, EU and Australia. Manage pre-submissions, technical documentation (510(k), technical files), postmarket surveillance, CAPAs, recalls, and regulatory impact assessments. Serve as RA subject-matter expert on cross-functional teams, liaise with regulators (FDA, Notified Bodies, TGA), and ensure compliance with relevant standards and company QMS.