Machine Learning Engineer

Role Overview

An established SaaS company is looking for a Machine Learning Engineer to help design, build, and support machine learning solutions that solve practical business problems. This role works across data science, engineering, and business teams to turn models into dependable, scalable AI products and improve the performance of data-led services.

Core Responsibilities

• Create, test, and refine machine learning models using supervised, unsupervised, and reinforcement learning methods.
• Prepare raw data by cleaning, transforming, and structuring large datasets for training and inference.
• Improve model performance through algorithm tuning and optimisation.
• Set up reliable production deployment processes for machine learning models with attention to scale and stability.
• Partner with data scientists to move prototypes into production and work with software engineers to embed models into existing systems.
• Track deployed models for accuracy and address issues such as data drift over time.
• Keep current with new ML techniques, tools, and industry practices to support modern solutions.
• Maintain clear documentation for processes, models, and tools to support repeatability and teamwork.

Experience and Technical Background

• Hands-on experience creating, deploying, and maintaining machine learning models in live production settings.
• Exposure to multiple ML approaches such as regression, classification, clustering, and neural networks.
• Working knowledge of deep learning libraries like TensorFlow or PyTorch, along with traditional ML tools such as Scikit-learn or XGBoost.
• Strong coding ability in Python, R, or a similar language.
• Experience using data analysis libraries such as Pandas and NumPy.
• Familiarity with Azure for training and deploying models in the cloud.
• Understanding of Docker and Kubernetes for containerisation and orchestration.
• Knowledge of database systems, including SQL and NoSQL.
• Exposure to big data technologies such as Hadoop, Spark, or Kafka would be an advantage.

Skills and Attributes

• Strong analytical thinking and problem-solving ability.
• Ability to explain complex technical ideas to non-technical stakeholders.
• Comfort working in a collaborative, fast-moving environment.
• Excellent communication skills and strong attention to detail.
• A genuine interest in innovation and practical AI solutions.

Education

A bachelor’s or master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related discipline is required. A PhD would be considered an advantage.

Work Style

The position is based in Melbourne, Victoria, Australia and follows a hybrid working arrangement.

Application Note

Applications are being invited for immediate consideration.