Top Machine Learning Jobs in Melbourne

The Staff Engineer leads the technical strategy and development of AI and ML platforms, ensuring innovation and high availability in multi-cloud environments.

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

As an Engineering Manager, you'll lead a team developing AI-powered help platform capabilities, focusing on scalable backend systems and engineering quality while driving collaboration across functional teams.

As a Machine Learning Engineer, develop ML models for computer vision, integrate with products, manage data and experiments, and work on end-to-end ML operations.

The Lead Metallurgist will oversee mineral resource evaluation, ensure quality control, audit geometallurgical models, and provide technical leadership, focusing on optimizing mine operations through advanced modeling and collaboration with teams.

The Director of Business Development will lead business development and go-to-market initiatives in mining, leveraging industry connections to establish executive relationships and create opportunities for Stratum AI's solutions. Key responsibilities include market development, sales strategy creation, and pipeline development, targeting high-value clients and navigating complex sales environments.

The Lead Metallurgist will oversee resource evaluation, audit geometallurgy models, liaise with teams, ensure quality control, and mentor junior staff.

As a Staff Machine Learning Engineer, you'll drive AI integrations, develop evaluation frameworks, shape architecture, and enhance AI-driven design interactions at Canva.

As a Senior MLOps Engineer, you will enhance ML operations by designing and maintaining infrastructure, implementing CI/CD pipelines, and collaborating with data scientists to streamline workflows.

The Digital Solution Executive will drive sales of digital solutions, design proposals, and manage complex deal logistics and negotiations.

Design and implement robust, large-scale distributed ML training systems optimized for low-bandwidth, high-latency environments. Build model-parallel training strategies, checkpointing and recovery, GPU and memory optimizations, P2P networking, NAT traversal, and monitoring to ensure resilient, efficient multi-participant training.