[Hiring] Machine Learning Engineer @Canibuild

Role Description

We are looking for a Machine Learning Engineer with strong experience in Computer Vision and document understanding to help build AI-driven capabilities within our property technology platform. The role focuses on developing models that analyze visual and document-based data to extract structured information and support automated decision-making within our products.

• Design, develop, and deploy computer vision and machine learning models for analyzing visual and document-based data.
• Build pipelines that convert unstructured visual inputs into structured and usable information.
• Develop and evaluate models for tasks such as object detection, segmentation, document parsing, and image understanding.
• Apply OCR and related techniques to extract meaningful information from complex documents and imagery.
• Work with large datasets and build efficient training and evaluation pipelines.
• Handle real-world visual datasets that may contain noise, inconsistencies, incomplete information, or varying formats.
• Experiment with different approaches to solve challenging computer vision problems and evaluate tradeoffs between accuracy, performance, and complexity.
• Collaborate with product and engineering teams to integrate machine learning models into scalable production systems.
• Continuously improve model performance, accuracy, and robustness in real-world environments.
• Stay up to date with the latest developments in AI and computer vision and apply relevant techniques where appropriate.
• Actively leverage modern AI tools and frameworks to accelerate experimentation, development, and engineering workflows.

Qualifications

• 5+ years of hands-on experience building and deploying machine learning models, particularly in Computer Vision or document understanding.
• Strong proficiency in Python for machine learning and data processing.
• Hands-on experience with modern ML frameworks such as PyTorch and libraries in the Hugging Face ecosystem.
• Experience with computer vision tooling such as OpenCV.
• Experience with common ML and data science libraries such as scikit-learn, NumPy, and Pandas.
• Experience developing models for tasks such as segmentation, object detection, or document analysis.
• Experience working with large image datasets and building training pipelines.
• Solid understanding of model evaluation, data preprocessing, and performance optimization.
• Strong problem-solving skills and ability to work in a fast-paced product environment.
• Ability to collaborate effectively with cross-functional engineering and product teams.
• The candidate should be based in Australia.
• Willing to work remotely full-time.
• Work schedule is Mon to Fri, 9:00 AM to 5:00 PM AEST.

Preferred Qualifications

• Experience with TensorFlow or other deep learning frameworks.
• Experience working with OCR pipelines or document analysis systems.
• Experience deploying machine learning models in production environments.
• Experience with containerized deployments such as Docker or Kubernetes.
• Experience working with complex technical documents, diagrams, or structured visual data.
• Familiarity with spatial or geometry-related data problems.
• Experience with libraries such as Detectron2, MMDetection, or similar.
• Familiarity with frameworks used to integrate modern AI models into applications (e.g., LangChain or similar tooling).
• Contributions to open-source ML or computer vision projects are a plus.

Additional Information

• The problems we work on involve complex visual and document-based data, so we value engineers who enjoy tackling challenging technical problems and experimenting with different approaches to reach practical solutions.
• Candidates are required to include link to relevant projects, GitHub repositories, research work, or examples of machine learning systems they have built.

Benefits

• Flexible remote work opportunities with career development opportunities.
• Engagement with a supportive and collaborative global team.
• Competitive market-based salary.