Top Machine Learning Jobs in Melbourne

As a Senior Machine Learning Consultant at DiUS, you'll work directly with clients to solve business problems using AI/ML technologies. Your role includes developing custom models, deploying them in cloud environments, and ensuring integration into broader solutions. You'll collaborate with a skilled team while leveraging your expertise in machine learning frameworks and practices.

As a Senior Machine Learning Engineer at Leonardo.Ai, you will develop and maintain MLOps pipelines, integrate cloud technologies, and optimize machine learning model deployment and monitoring. Collaborating with cross-functional teams, you'll ensure efficient workflows while driving innovation in AI product development.

Develop and deploy scalable AI models, optimize ML algorithms, and collaborate with teams to integrate AI solutions into production systems.

As a Senior AI/ML Engineer at Nansen, you will develop and monitor AI models to analyze blockchain data, collaborate with cross-functional teams to enhance the platform's analytical capabilities, and mentor junior engineers. Your work will significantly influence decision-making for crypto investors and drive the technology forward.

In this role, you will develop, deploy, and optimize machine learning solutions, collaborating with teams to drive business value. Responsibilities include designing ML models, integrating them into production, optimizing systems, and continually improving processes with new tools and frameworks.

The Machine Learning Engineer at Tricentis will design and implement machine learning systems, assist in data preparation, contribute to team workflows, and ensure optimal integration of AI solutions into production applications, all while fostering team collaboration and maintaining high code performance.

As a Machine Learning Engineer at nib Group, you will develop, integrate, and deploy innovative machine learning models while collaborating with cross-functional teams. Your role will involve software engineering, model monitoring, and maintenance, ensuring seamless data pipelines, and enhancing the Intelligent Document Processing solution.

As a Senior Machine Learning Engineer, you will develop MLOps pipelines, manage cloud infrastructure, design data pipelines, and deploy ML models while collaborating with cross-functional teams to innovate AI products.

The MLOps Engineer will establish and maintain AI/ML infrastructure, focusing on automating workflows and optimizing operations for model reliability. Responsibilities include implementing CI/CD pipelines, deploying scalable ML infrastructure, and supporting data engineering efforts while ensuring compliance and security standards are met.

The Senior Machine Learning Developer will design, build, and optimize machine learning models for AI-driven site planning tools. Responsibilities include developing scalable ML solutions, optimizing real-time models, and collaborating with engineering teams to meet business needs.

As a Machine Learning Engineering Team Lead at Leonardo.Ai, you will mentor and lead a diverse team, define MLOps strategies, optimize AI deployability, oversee AWS infrastructure, develop scalable data pipelines, and implement monitoring for machine learning models. Your focus will be on creating an inclusive environment and driving impactful AI initiatives.

The ML Ops Engineer will set up and support ML bot inference in live environments, implement CI/CD for ML solutions, and enhance ML inference logic.

As a Software (ML Product) Engineer, you will enhance the user experience of ML tools by optimizing workflows and processes. You will collaborate with technical product managers, contribute to defining efficient ML practices, and improve DVC, an open-source tool for machine learning. A strong background in deep learning and system design is necessary to transform current workflows and engage with the ML community.

As a Senior Machine Learning Engineer, you'll bridge the gap between data science and ad systems engineering, developing solutions for model experimentation, monitoring, and pipelines, while driving innovation and best practices in machine learning systems.

As a Software Engineer, you will develop end-to-end data analytics and MLOps solutions using open-source tools, collaborate with a distributed team, debug issues, and maintain cloud-deployed solutions for public and private infrastructures.