AI / ML Infrastructure Engineer (MLOps) – Robotics - Hybrid in Bristol - Upto £110,000
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