AI / ML Infrastructure Engineer

AI / ML Infrastructure Engineer (MLOps) – Robotics - Hybrid in Bristol

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

Desirable:

• Robotics, autonomous systems, or embodied AI experience
• GPU orchestration (Kubeflow, Kubernetes, SkyPilot)
• Edge deployment (ONNX, TensorRT)

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
• Seniority Level
• Not Applicable
• Industry
• IT Services and IT Consulting
• Employment Type
• Full-time
• Job Functions
• Information Technology Engineering Skills
• Python (Programming Language)RoboticsArtificial Intelligence (AI)InfrastructureMachine Learning

Apply now!