AI/ ML Infrastructure Engineer

We’re working with a cutting-edge robotics company building intelligent systems capable of learning real-world physical tasks.

They’re now hiring an AI / ML Infrastructure Engineer to own the end-to-end infrastructure that powers model training, data pipelines, and deployment into real-world robotic systems.

This is a highly technical role sitting at the intersection of machine learning, distributed systems, and robotics – not a generic MLOps position.

Key Responsibilities

• Build and scale GPU-based training infrastructure for large ML workloads
• Develop robust data pipelines for multi-modal datasets
• Own experiment tracking, model versioning, and reproducibility
• Design and optimise model deployment pipelines (including edge inference)
• Improve CI/CD workflows for ML systems and automate infrastructure

Key Requirements

• Strong Python and experience with PyTorch-based training pipelines
• Experience with distributed training (DDP, FSDP, DeepSpeed)
• Solid cloud experience (GCP / AWS / Azure)
• Hands-on with Docker and infrastructure-as-code (Terraform)
• Experience building ML pipelines in production environments
• Robotics, autonomous systems, or embodied AI experience

Why Apply?

• Work on real-world AI systems deployed into physical robots
• Direct impact on cutting-edge robotics capability
• Fast-moving, high-calibre engineering environment

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