Job Description
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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 xwzovoh 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!