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.

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.

Design and deliver enterprise AI architectures spanning generative AI, classical machine learning, LLMs, and agentic systems. Lead architecture for agent platforms, model serving, RAG, data pipelines, security, governance, observability, FinOps, and AI operations. Define reference architectures and reusable patterns, build proof-of-concept software, produce architecture documentation, guide stakeholder decisions, and mentor cross-functional engineering teams.

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.

Leads the strategy, roadmap, delivery, lifecycle, adoption, governance, security, and measurement of enterprise data products. Translates business priorities into delivery plans, manages backlogs and dependencies, coordinates engineering and external partners, and ensures data products are trusted, usable, compliant, and valuable. The role also advances a Data as a Service model and supports cross-functional stakeholder alignment across data, technology, analytics, and business teams.

Provides technical leadership for secure, resilient cloud-native content platforms. Responsibilities include designing and operating AWS and Kubernetes solutions, developing software and automation, integrating AI capabilities, improving CI/CD and infrastructure-as-code practices, embedding testing and observability, strengthening reliability and disaster recovery, and mentoring engineers. The role supports the migration and ongoing enhancement of OpenText document retention and archiving services.

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.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Lead the data organization powering frontier AI models. Build and manage a team of data and ML engineers, design web-scale crawling and ingestion architecture, develop pipelines for filtering and deduplication, secure proprietary Australian datasets, and establish data governance, privacy, and security standards. Collaborate with model-training teams to refine data mixtures and quality based on model performance.

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.

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.

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.

Design, build, test, deploy, and monitor reliable AI/ML products and services at scale. Develop real-time and batch data pipelines, optimize ranking and retrieval models using embeddings, transformers, and LLMs, and establish evaluation, observability, and model lifecycle management practices. Collaborate across product, engineering, data, and consumer experience teams while contributing to scalable ML platform capabilities.