Overview
This fast-growing, private-equity-backed SaaS business is redefining how asset-intensive industries harness data and AI to drive reliability and performance. Their enterprise platform is trusted by global leaders, delivering smarter, faster, and more accurate insights for large-scale operations. With a modern technology stack, a collaborative engineering culture, and an ambitious growth trajectory, they offer the chance to help shape the future of industrial intelligence.
The Role
As a Full Stack Machine Learning Engineer, you'll join a high-performing R&D team working on mission-critical AI solutions for predictive maintenance and operational optimisation. Reporting to the Manager of AI & Data Science, you'll design, build, and deploy production-grade ML models that power real-time decision support and automation across complex environments. You'll work hands-on with one of the world's richest industrial datasets, collaborating with domain experts and product teams to deliver solutions that make a measurable impact in the field.
About You
You're a technically strong and ambitious ML engineer who thrives in collaborative, fast-paced environments. You enjoy solving complex problems, building scalable solutions, and working across modern frameworks and cloud platforms. With a solid grounding in end-to-end ML development and a passion for deploying models that deliver real-world results, you're eager to keep learning and contribute ideas that push the boundaries of industrial AI.
Skills & Experience
3+ years of experience in machine learning engineering, including at least 1 year in industrial applications (IoT, predictive analytics, fleet telemetry, or similar)
Strong proficiency in Python for ML development
Experience with ML frameworks (TensorFlow, PyTorch) and libraries (scikit-learn, Hugging Face)
Proven track record of deploying ML models into production (time-series forecasting, anomaly detection, classification)
Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, or similar) for model versioning, monitoring, and retraining
Familiarity with agentic AI concepts (multi-agent systems, human-in-the-loop implementations)
Understanding of data pipelines and ETL for multimodal datasets (sensor, image, text, telemetry)
Cloud deployment experience (AWS, Azure, or GCP) with containerisation (Docker, Kubernetes)
Knowledge of interpretable ML techniques (e.g., SHAP, LIME)
Valuable extras
Experience with predictive maintenance or condition monitoring
Exposure to computer vision (thermal imaging, defect detection)
Familiarity with graph neural networks or multimodal AI
Contributions to open-source ML projects or publications in industrial AI
What's on Offer
Competitive salary and performance bonus
Flexible hybrid working arrangements
Dedicated professional development opportunities
Inclusive, collaborative company culture
Support for work-life balance, including extra leave and wellbeing initiatives
Apply
If you're ready to make an impact in a fast-growing SaaS business and help deliver AI solutions that transform asset reliability and performance, apply today Click "Apply Now" or reach out to the Brisbane office for a confidential discussion.
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