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 is already being used on major construction projects around the world to help teams build more accurately, efficiently and with fewer mistakes.
Now, we’re taking that technology even further.
We’re developing the next generation of wearable AI systems, combining computer vision, localisation, BIM understanding, semantic reasoning and augmented reality to create a Construction Intelligence platform capable of understanding what has been built, where it has been built and whether it has been installed correctly.
As a Series B business entering our next phase of growth, we’re investing further in our R&D capability and building out a new technology hub in Calgary, Canada.
We’re looking for an AI Platform & Machine Learning Engineer to join us and help build the infrastructure that will power the next generation of our AI products.
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.
Support federated learning and improve model lifecycle management.
Deploy AI models across cloud services and embedded NVIDIA platforms.
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.
Experience deploying machine learning models within cloud environments.
Strong software engineering fundamentals and an understanding of how to build reliable, maintainable production systems.
Comfortable working in a fast-moving area where technologies and approaches continue to evolve rapidly.
Experience with technologies such as MLflow, Weights & Biases, Ray, Kubeflow, Airflow, LoRA, PEFT, NVIDIA Jetson, CUDA, TensorRT or ONNX would be highly valuable, as would experience with AWS, Azure or GCP and large-scale dataset management.
You don’t need to have worked with every technology on the list. We’re particularly interested in people who can demonstrate strong ML engineering fundamentals, genuine hands-on experience and the ability to learn and adapt as the technology evolves.
Above all, we’re looking for someone who is curious, collaborative and excited by solving difficult technical problems – someone who wants to help turn cutting-edge AI research into technology that works in the real world.
What You Could Be Working On
From the outset, you’ll have the opportunity to contribute to high-priority projects including:
AI training infrastructure
Continual learning platform
Model registry and experiment tracking
Automated benchmarking frameworks
Cloud-to-edge deployment pipelines
Embedded AI deployment
Wearable Construction Intelligence technology
🚀 Work at the intersection of AI, Computer Vision, AR and real-world engineering
🧠 Help build production AI systems tackling genuinely complex technical problems
🏗️ See your work applied to some of the world’s most ambitious construction projects
📈 Join a Series B business at an exciting stage of international growth
🔬 Work alongside specialists across AI, Computer Vision, Navigation and Embedded Engineering
💡 Have meaningful input into the architecture and evolution of our next-generation AI platform
🌍 Build technology with real-world, global impact
If you’re excited by the challenge of taking machine learning from experimentation through to scalable production, and want to help build the AI infrastructure behind the future of construction, we’d love to hear from you.