About Us

Canibuild automates the residential construction industry’s design, approval, and sales processes, allowing clients to answer 'Can I build this on this plot of land?' instantly. As a fast-growing SaaS platform backed by Australia’s largest hedge fund, we serve clients across Australia, New Zealand, Canada, and the US.

Job Overview

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.

You will work closely with product and engineering teams to design, build, and deploy machine learning solutions that operate reliably in production environments.

We are particularly interested in engineers who have taken machine learning systems from experimentation to production and are comfortable solving messy real-world data problems.

Key Responsibilities

• 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.

Requirements

• 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