Full Stack Machine Learning Engineer

Join a rapidly growing SaaS company delivering AI-powered predictive maintenance and asset reliability solutions for large-scale industrial operations. Be part of an ambitious R&D team leveraging decades of curated machine health data and live sensor streams to build and deploy production-grade ML models that make a real impact in the field.

The Role

As a Full Stack Machine Learning Engineer, you’ll design, build, and deploy ML models for predictive maintenance, anomaly detection, and time-series forecasting. You’ll develop robust data pipelines for multimodal data (sensor, image, text, fleet telemetry), and collaborate with engineers and product teams to deliver scalable, reliable AI solutions. You’ll work hands-on with cloud technologies and modern MLOps tools to ensure models are deployed, monitored, and retrained efficiently.

Responsibilities

• Conduct exploratory data analysis and develop ML models for industrial applications
• Build and maintain data pipelines for sensor, image, and telemetry data
• Prototype, optimize, and validate models for Remaining Useful Life (RUL) estimation and condition-based maintenance
• Design and refine AI agents for automated equipment monitoring and decision support
• Deploy ML solutions using CI/CD-enabled MLOps pipelines (MLflow, Kubeflow, SageMaker)
• Ensure ML models are interpretable, explainable, and trusted by end-users
• Collaborate with domain experts, data engineers, and product teams

About You

• 3+ years of ML engineering experience, including at least 1 year in industrial applications (IoT, predictive analytics, fleet telemetry, etc.)
• Strong proficiency in Python for end-to-end ML development
• Experience with ML frameworks (TensorFlow, PyTorch) and libraries (scikit-learn, Hugging Face)
• Hands-on experience with MLOps tools for model versioning, monitoring, and retraining
• Cloud deployment experience (AWS, Azure, GCP) with containerisation (Docker, Kubernetes)
• Familiarity with agentic AI concepts and human-in-the-loop implementations
• Understanding of data pipelines and ETL for multimodal datasets
• 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 Now

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.