AI/ ML Infrastructure Engineer

OpenSourced - Search & Selection; Bristol; Hybrid; GBP 99,000 - 121,000; Full time

Benefits offered by this job

Work on real-world AI systems

Direct impact on robotics capability

Fast-moving engineering environment

Job summary

OpenSourced - Search & Selection is looking for a highly skilled AI / ML Infrastructure Engineer in Bristol to lead the development of infrastructure for AI systems in robotics. This technical role requires expertise in Python and cloud technologies to enhance model training and deployment processes.

Ideal candidates will work in a fast-paced environment to build GPU-based training infrastructures and develop data pipelines that directly influence cutting-edge robotics capabilities.

Qualifications

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

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 optimize model deployment pipelines, including edge inference.
• Improve CI/CD workflows for ML systems and automate infrastructure.

Skills

Strong Python

Experience with PyTorch-based training pipelines

Experience with distributed training

Solid cloud experience

Hands-on with Docker

Infrastructure-as-code experience

Experience building ML pipelines

Robotics or autonomous systems experience

Tools

Docker

Terraform

Job description

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
• Robotics, autonomous systems, or embodied AI experience

Benefits

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