Machine Learning Infrastructure Engineer

London

Up to £150000

About the Role

A cutting-edge AI start-up is pioneering the development of frontier 3D foundation models, pushing the boundaries of computer vision and spatial computing. Their mission is to redefine how industries such as robotics, AR/VR, gaming and film generate and interact with 3D content.

ML & Cloud Infrastructure Engineer

This is a unique opportunity to work at the forefront of AI innovation, building the infrastructure that underpins complex ML workloads and production systems. You’ll play a central role in scaling the company’s platforms and ensuring their pioneering technology reaches its full potential.

Key Responsibilities

• Develop and maintain scalable, high-performance cloud-based infrastructure for ML workloads and API deployment.
• Manage and optimize cloud platforms (AWS, Azure, GCP) and set up ML nodes for local and distributed training.
• Install, configure, and monitor servers, ensuring system reliability.
• Design and optimize storage solutions for large-scale ML datasets.
• Manage containerized applications with Docker, Kubernetes, Terraform, and related tools.
• Collaborate with ML engineers and researchers to ensure seamless orchestration of training and production environments.
• Troubleshoot and respond to cloud/production incidents, implementing long-term solutions.

What We’re Looking For

• At least 3 years of professional experience in a cloud-related engineering role (ML-related experience highly desirable).
• Proven expertise in at least one major cloud platform (AWS, GCP, or Azure).
• Experience with containerization and orchestration (Docker, Kubernetes).
• Ability to manage and optimize large-scale cloud infrastructure.
• Familiarity with Python (Jupyter) and ML frameworks (e.g., PyTorch).
• Experience with cloud monitoring tools (Prometheus, Grafana).
• Exposure to cloud-based databases (RDS, Aurora, Spanner, etc.) and data-visualisation tools.
• Knowledge of CI/CD tools (e.g., CircleCI).