Top Machine Learning Jobs in Melbourne

The role involves deploying and supporting CrowdStrike's AI capabilities for threat detection and response, along with mentoring and customer engagement.

As a Senior Data Scientist, you will develop machine learning models and predictive analytics to shape the growth strategy, enhancing customer acquisition, engagement, and retention for Airwallex.

Lead the AI product team to innovate financial services, managing AI features and automation to enhance customer experience and efficiency.

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.

The Senior Data Scientist will lead the development of machine learning models, conduct data analysis, and collaborate with cross-functional teams to optimize services and tools at Mable.

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

The Creature Supervisor oversees hero creature and character work in VFX, collaborating with multiple teams, ensuring quality and efficiency, and promoting innovative solutions.

As an AI Research Engineer, develop reinforcement learning algorithms, optimize decision-making, debug and enhance learning pipelines, and integrate models into production systems.

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 collaborative molecular imaging research projects focused on PET (and SPECT), design feasibility studies, develop and validate prototypes, publish and present findings, grow external partnerships, and translate research tools into product-ready solutions.

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

This role leads enterprise-scale AI and Data transformation programs, acting as a trusted advisor to senior executives, and shaping data strategies and governance frameworks.

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.

As a Senior Machine Learning Engineer, develop ML models to digitize infrastructure from varied data sources while optimizing training pipelines and mentoring junior engineers.

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.

The Director of Corporate Strategy leads strategic initiatives, develops long-term plans, and manages cross-functional projects while engaging stakeholders and producing executive materials.

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.

Contribute to ML pipelines, support model deployment, collaborate with teams, automate workflows, troubleshoot issues, and apply MLOps practices.

The Senior Machine Learning Engineer will own the end-to-end ML lifecycle, design and deploy models, and directly engage with customers to develop AI/ML solutions, focusing on enhancing developer experiences.

The role involves designing, building, and scaling ML/RL systems that enhance customer experience while ensuring reliability and efficiency in production. Responsibilities include model optimization, deployment, and monitoring for performance improvements.