Top Machine Learning Jobs in Melbourne

The Head of International Credit and Data Science will lead the analytics team, developing risk models and credit policies to drive underwriting strategies and innovative product launches in Latin America and South East Asia. The role focuses on leveraging data to enhance business growth and improve customer financial health, while adapting solutions for diverse markets.

As a Machine Learning Engineer, you will develop user-driven ML features for Canva's Video editor, implement ML models, and run experiments. Collaborate with specialists to improve model performance and contribute to data analysis, preprocessing, and productization of ML/data models.

As the Analytics Principal, you will drive analytical strategies within the Retail team, develop end-to-end data products, lead a team of data specialists, and oversee statistical modelling and analysis to extract insights from datasets.

The role involves leading ML and MLOps initiatives, designing and deploying solutions on AWS while ensuring architecture governance and managing complex model/data pipelines to support Data Scientists. Strong communication and collaboration skills are essential to work with stakeholders and present technical information effectively.

As a Software (ML Product) Engineer at iterative.ai, you will enhance user workflows for DVC, collaborate with technical product managers, and improve MLOps practices within ML teams. Responsibilities include optimizing the tool’s usability and driving projects like dvclive, emphasizing software engineering skills and communication.

The Director of Engineering will lead a global engineering team to enhance product strategy, overseeing engineers working on client innovations and collaborating with product leadership. Responsibilities include talent development, executing product strategy, removing roadblocks, and driving delivery goals while ensuring high-quality engineering processes.

The Software Developer I will design, develop, and implement software products for business projects, collaborating with teams and stakeholders. Responsibilities include coding, testing, quality assurance, and maintaining documentation while staying updated on new technologies and best practices.

The AI/ML and MLOps Field Engineer at Canonical will assist global companies in adopting AI using open source technologies on cloud infrastructures, primarily focusing on Linux and Kubernetes. Responsibilities include architecting cloud solutions, engaging in customer interactions, and solving complex data architecture problems using various data technologies.