Job description

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. - 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 or PEFT would be highly valuable, as would experience with edge inference and GPU optimisation (e.g. CUDA, TensorRT, ONNX Runtime), a major cloud provider 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 - AI for real-world construction environments. WHY JOIN US - 🚀 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 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.

Requirements

Must-haves

Help build the AI platform powering the future of construction
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.
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.
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.
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.
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.

Nice-to-haves

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

Application prep

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Keywords to mirror

aimachine learningci/cdpythonmldockerkubernetes

Platform: ashby
Effort: quick

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