Top Machine Learning Jobs in Melbourne

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.

Build and improve core AI and machine learning infrastructure enabling teams to develop, train, evaluate, deploy, and operate ML models and pipelines. Design scalable distributed systems, support end-to-end ML lifecycles, implement MLOps automation, collaborate with product engineering teams, lead projects from design through launch, review code, document solutions, and mentor junior engineers. Work may include dataset curation, LLM fine-tuning, model serving, monitoring, and generative AI systems.

Design, build, deploy, and operate agentic LLM harnesses powering Rovo AI features. Improve production model quality, reliability, and cost; develop evaluations, datasets, benchmarks, fine-tuning, and model-serving capabilities. Shape long-term LLM engineering direction, lead technical initiatives, influence stakeholders, guide virtual squads, and coach junior engineers.

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 product strategy and roadmaps for AI Enrichment and Listings Enrichment squads. Own audience modelling and segmentation vision, prioritize data and ML capabilities, build investment cases, and balance reusable platform development with near-term outcomes. Partner with product, engineering, and data science teams on model lifecycles, experimentation, evaluation, monitoring, and adoption. Champion data quality, privacy, responsible AI, and measurable business impact across REA’s consumer and customer experiences.

Own the vision, strategy, roadmap, and delivery of enterprise identity graph and security signal products. Translate identity, data, AI, and security needs into measurable outcomes, prioritize capabilities, lead cross-functional engineering delivery, manage dependencies and risks, and communicate progress to senior stakeholders. The role focuses on connected identity data, graph analytics, real-time risk signals, and automated security outcomes.

Drive revenue through complex enterprise sales by designing cloud data security solutions, conducting proof-of-value engagements and technical demonstrations, consulting with executive stakeholders, supporting partners, and coordinating internal resources. The role requires expertise in cloud security, data protection, IAM, networking, databases, encryption, privacy, and regulatory frameworks, along with strong presentation and problem-solving skills. Travel up to 50% within the assigned territory is required.

Sessional economics tutors and markers will teach or assess undergraduate and postgraduate economics and econometrics courses. Applicants may contribute expertise in areas such as microeconomics, behavioural economics, game theory, digital economics, time series analysis, big data, machine learning, and blockchain. Successful applicants will engage students positively, support learning, and may be considered for future casual research opportunities. Work is casual and dependent on course demand and timetabling.

Casual sessional teaching and marking opportunities in finance at RMIT’s School of Economics, Finance and Marketing. Successful applicants may teach undergraduate or postgraduate subjects including risk management, sustainable finance, portfolio management, financial modelling, fintech, quantitative finance, trading, and machine learning in finance. Responsibilities include engaging students, facilitating learning in classrooms or the RMIT Trading Facility, and potentially contributing to marking or future research activities.

Develop, deploy, and maintain data science products for Shell Energy. Build machine learning and advanced analytics models for customer churn, load behavior, forecasting, demand response, and energy optimization. Collaborate with stakeholders, engineers, analysts, and technology teams to deliver scalable solutions. Apply DevOps principles, monitor model performance, troubleshoot technical issues, and communicate actionable insights to technical and business audiences.

Lead OES’s enterprise analytics, business intelligence, forecasting, and data science functions. Develop analytics strategy, KPI governance, reporting, predictive modeling, and AI initiatives that improve student outcomes and organizational performance. Partner with executives and operational stakeholders to translate complex data into actionable recommendations, promote self-service analytics and data literacy, and build scalable solutions. Recruit, mentor, and lead a high-performing Analytics and Data Science team while continuously improving analytics capabilities and organizational maturity.

Leads strategy and execution for pricing, driver matching, and incentive products supporting delivery operations across international markets. Owns product vision, roadmaps, discovery, experimentation, analytics, launch execution, and value realization. Partners with Engineering, Design, Analytics, and business stakeholders to optimize driver experience, delivery performance, and cost per order. Influences technical direction, evaluates AI opportunities, mentors product managers, and addresses local regulatory and market requirements in Canada and Latin America.

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 and deploy data science, AI, and machine learning solutions, including LLM, RAG, and agentic workflow applications. Evaluate model performance, scalability, and stability; transition prototypes into production with Product, Engineering, and Data Engineering teams. Apply privacy, security, and responsible AI practices, monitor industry trends, and communicate technical recommendations to varied stakeholders.

Senior Data Scientist responsible for designing, deploying, and evaluating AI, machine learning, LLM, RAG, and agentic solutions. The role translates business problems into scalable data products, establishes responsible AI and testing practices, collaborates with engineering and product teams, communicates technical decisions, and mentors junior data scientists. Candidates need strong Python, SQL, statistics, machine learning, cloud, productionization, and stakeholder communication skills.

Build and manage Pattern’s Australian strategic partner ecosystem across ecommerce, martech, agencies, and technology providers. Generate partner-sourced pipeline through referrals, co-selling, joint marketing campaigns, events, and account planning. Own partner relationships, activate new partnerships, coordinate go-to-market initiatives, and track commercial performance. Collaborate closely with Sales and Marketing to convert partner relationships into qualified opportunities and measurable revenue growth.

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 enterprise Data and AI transformation engagements using Databricks across AWS and Azure. Define data strategies, roadmaps, governance frameworks, and modern Lakehouse architectures. Design AI-enabled data products using Mosaic AI, GenAI, RAG, Vector Search, MLflow, and agentic AI. Advise executive stakeholders, facilitate architecture workshops, support solution pursuits, and develop reusable architecture patterns for the Data and AI practice.

Designs and delivers NVIDIA-based generative AI solutions for enterprise customers and partners. Responsibilities include leading technical engagements, developing proofs of concept and demonstrations, writing Python for data and AI pipelines, designing scalable architectures, deploying workloads on Kubernetes, enabling customers through training, and coordinating engineering, product, sales, and external stakeholders. The role requires expertise in data science, deep learning, LLMs, RAG, agentic AI, LangChain, LangGraph, GPUs, and customer-facing technical consulting.

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.

Build, optimize, and deploy LLM-powered agentic workflows for enterprise customers. Lead high-stakes AI delivery projects, understand customer needs, develop AI strategies, implement production solutions, and drive adoption. The role combines hands-on engineering, machine learning, customer collaboration, and onsite deployment, with travel often exceeding 50%.

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.

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.

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.