AI Platform & Machine Learning Engineer

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.

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.

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.