Top Machine Learning Jobs in Melbourne

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.

Serve as the product specialist for Zero Trust Networking across Australia and New Zealand, driving adoption and revenue for ZTNA, SD-WAN, and branch solutions. Collaborate with solution engineers and account teams to gather requirements, create value propositions, close deals, implement account-based strategies, and expand partner and customer relationships. Operate in a hybrid model (Sydney/Melbourne) and travel extensively as needed.

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.

Lead design and implementation of large-scale distributed production systems powering generative AI features. Own architecture, mentor engineers, manage technical debt, operationalize ML/LLM models, and collaborate with scientists to productionize research using Python, SQL, Spark/Dask, AWS, and Kubernetes.

The Reserve Engineer maintains and services robotic hardware, responds to failures at client sites, and ensures customer satisfaction. Responsibilities include learning hardware, communicating repairs, and assisting with deployment projects.

Design and build scalable ML infrastructure and services to productionise models. Develop data orchestration pipelines, support real-time AI services, and collaborate with scientists and engineers to deliver LLM-powered features for millions of users.

The Head of Data will lead the data organization, define the company-wide data strategy, ensure high-quality decision making, collaborate across teams, mentor leaders, and establish best practices in data governance and execution.

Drive AI strategy and transformation by identifying high-value AI opportunities, defining roadmaps, operating models, and governance, facilitating workshops, building business cases, and supporting adoption, change management, and value realisation across business and technology stakeholders.

Lead client engagements to define and execute enterprise AI strategies, operating models, governance, and transformation roadmaps. Identify and prioritise high-value AI use cases, build business cases, drive adoption, mentor teams, and ensure measurable business outcomes across growth, efficiency, customer experience, and risk management.

Support Emiri client deployments by bridging product support, UX, and development: investigate issues, triage root causes, configure and QA client models, coach commercial teams, test fixes, and communicate feedback to product. Use Jira/Confluence and apply NLP/ML knowledge to improve processes and user education.

The Senior Data Scientist will analyze and interpret large datasets, develop deployable solutions, and lead projects. Responsibilities include designing predictive models, evaluating peer work, and communicating results.

Partner with business leaders to translate complex problems into analytical frameworks, deliver data-driven insights and visualisations, lead end-to-end analytical projects, and manage a team to improve portfolio performance and strategic decision-making.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

The Senior Account Executive will develop sales strategies, cultivate relationships with clients, and manage the sales process to close deals with banks and fintechs.

Lead a Data Analyst team to analyze PB-scale transactional and customer data, build algorithmic systems and visualizations, implement ML/LLM solutions for compliance use cases, support business stakeholders with data requests, and translate findings into actionable recommendations.

The Genesys Cloud Architect will lead the design and architecture of cloud-based contact center solutions, ensuring alignment with business strategies and technical requirements.

As a Strategic Sales Director, you will lead sales efforts, develop relationships with key executives, and drive significant transactions in Australia.

Design and implement end-to-end data solutions: build data pipelines, perform data modelling and warehousing, implement ETL, write scalable Python/SQL/Spark code, work with cloud data services, support ML/AI initiatives, and liaise with clients to deliver practical, secure data products and visualisations.

Lead design and implementation of AI research and production infrastructure. Mentor junior engineers, and collaborate to enhance data usability, solving complex technical challenges.

Lead end-to-end solution architecture for data, analytics and AI initiatives. Design scalable, secure cloud data platforms and AI solutions, embed governance and responsible AI, guide delivery teams, manage architectural risk and technical debt, and evolve enterprise data and AI architecture roadmaps and standards.

The Solution Sales Executive will drive sales of FICO's platform in financial services, engaging with clients, managing relationships, and collaborating with internal teams to deliver solutions.

Lead enhancement of risk infrastructure and automation, analyze historical trading data, administer MT4/MT5 and third-party trading tech, collaborate on hedging and mitigation strategies, prepare reports for Head of Risk, and mentor team members.

As a Senior Software Engineer, you'll design and implement features related to digital twins, data abstractions, and structural analysis, leveraging machine learning and algorithms.

The Research Scientist will tackle advanced research problems in Protocol Learning, publish findings, and work collaboratively in a distributed model training environment.