Data Scientist / Machine Learning Engineer

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.

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.

• 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

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

Qualification

Bachelor's Degree