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 seeking a highly skilled Data Scientist / ML Engineer across levels with a strong background in Natural Language Processing (NLP) and Computer Vision. The ideal candidate should have 4+ years of experience working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Vision-Language Models (VLMs), and segmentation techniques. You will work closely with our product and engineering teams to develop and deploy AI-driven solutions that enhance our property-tech ecosystem.

Key Responsibilities

Design, develop, and deploy machine learning models focused on NLP and Computer Vision for property-related applications.
Implement and fine-tune LLMs for tasks such as document processing, chatbot automation, and property insights.
Build and optimize RAG pipelines to improve the accuracy and relevance of information retrieval in real estate data.
Develop computer vision models for segmentation, object detection, and VLM applications to enhance image and video analysis.
Work with structured and unstructured real estate data, including property listings, legal documents, and geospatial data.
Collaborate with cross-functional teams to integrate AI models into scalable production systems.
Stay up to date with the latest advancements in AI research and apply best practices to solve real-world challenges.
Optimize models for performance, efficiency, and deployment in cloud and edge environments.

Requirements

5+ years of hands-on experience in building and deploying ML models in NLP (LLMs, RAGs) and Computer Vision (segmentation, Vision Language Models).
Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Hugging Face, and OpenCV.
Experience working with vector databases, embedding models, and information retrieval techniques.
Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps best practices.
Solid understanding of data preprocessing, model evaluation, and performance optimization techniques.
Experience with multimodal AI models and working with large-scale datasets.
Strong problem-solving skills and ability to work in a fast-paced, dynamic environment.
Excellent communication skills and a passion for leveraging AI to transform the property-tech industry.

Preferred Qualifications

Experience in deploying models using containerization (Docker, Kubernetes) and microservices architectures.
Familiarity with GIS data processing and spatial analytics.
Contributions to open-source AI/ML projects or research publications in NLP/Computer Vision.

Benefits