Help build the AI platform powering the future of construction

At XYZ Reality, we've created the world's first engineering-grade Augmented Reality solution for construction. Our technology, The Atom, is already being used on major construction projects around the world to help teams build more accurately, efficiently and with fewer mistakes.

Our R&D team applies computer vision, spatial AI and machine learning to one of the most demanding real-world settings there is: the active construction site.

As a Series B business entering our next phase of growth, we're investing in our R&D capability and growing our team in Calgary, Canada.

We're looking for an AI Platform & Machine Learning Engineer to help build the infrastructure that takes our AI models from research into production.

The Role

This is an exciting opportunity for a Machine Learning Engineer who wants to work at the point where AI research meets real-world production engineering.

You’ll work on the systems that enable our AI models to move from research and experimentation into scalable, production-ready technology, building the infrastructure for dataset management, model training and retraining, experiment tracking, continual learning, deployment and monitoring.

A key part of the role will be understanding the complete AI lifecycle: from acquiring and labelling data through to training models, evaluating their performance and ultimately deploying them into production.

Working closely with our Computer Vision, Navigation, Embedded Software and Cloud Engineering teams, you’ll help ensure our AI models can be trained, evaluated, deployed and continually improved throughout the product lifecycle.

This is an exciting time to join! You’ll have the opportunity to work alongside deep technical expertise while having a genuine influence on how our AI platform evolves as we scale.

This isn’t AI for AI’s sake. The technology you build will ultimately support wearable systems being used in real-world construction environments, solving complex problems and changing how some of the world’s largest projects are delivered.

What You’ll Be Doing

Design and build scalable infrastructure for training and deploying machine learning models.

Develop distributed training pipelines and reproducible training environments.

Build experiment tracking, model versioning and model registry capabilities.

Develop automated benchmarking and model validation pipelines.

Build large-scale dataset ingestion, management and validation workflows.

Develop annotation and labelling workflows to support high-quality AI training data.

Explore synthetic data generation where appropriate.

Design and improve CI/CD pipelines for machine learning.

Monitor deployed model performance and support automated retraining.

Develop continual learning pipelines and explore techniques including PEFT and LoRA.

Improve model lifecycle management.

Deploy AI models across cloud and edge devices.

Optimise inference infrastructure and build scalable APIs supporting AI services.

Work closely with AI, Computer Vision, Navigation, Embedded and Cloud teams to turn research into production-quality systems.

Contribute to technical architecture decisions, engineering standards and automation across the wider R&D team.

What We’re Looking For

Minimum of 3–5 years’ experience in Machine Learning Engineering or a closely related role.

Degree in Computer Science, Artificial Intelligence, Software Engineering or a related discipline; Master’s level or above is preferred.

Strong Python development skills and hands-on experience with PyTorch.

Practical experience training and retraining machine learning models, we’re looking for someone who understands what happens beyond simply consuming existing AI models.

Experience building and maintaining production ML pipelines.

Strong understanding of the end-to-end ML lifecycle, from acquiring and preparing data through to training, evaluation, deployment and monitoring.

Experience with computer vision and modern AI/model architectures.

Experience with Docker, Kubernetes and CI/CD.