ML & Cloud Infrastructure Engineer

Harnham; London; Hybrid; GBP 140,000 - 160,000; Full time

Job summary

A leading tech recruitment firm is looking for a talented ML & Cloud Infrastructure Engineer in London. This hybrid role requires expertise in cloud engineering and machine learning. You will be responsible for building scalable infrastructure and ensuring the performance of crucial ML workloads. The ideal candidate has 6+ years in cloud environments, strong scripting skills, and experience with containerisation. This is a unique opportunity to work at the forefront of AI in 3D.

Qualifications

• 6+ years' experience in cloud engineering with ML workloads.
• Strong cloud skills with hands-on experience in AWS, GCP, or Azure.
• Experience in containerisation technologies like Docker and Kubernetes.

Responsibilities

• Build and maintain scalable cloud infrastructure for ML workloads.
• Set up ML nodes for distributed training and development.
• Manage containerised environments for production systems.

Skills

Cloud Engineering

Machine Learning

Scripting (Bash, PowerShell, Python)

Containerisation (Docker, Kubernetes)

Automation

Tools

AWS

GCP

Azure

Terraform

Prometheus

Grafana

Job description

Overview

This range is provided by Harnham. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Role: ML & Cloud Infrastructure Engineer
Salary: £140,000 - £160,000 + Equity
Location: London - Hybrid (3-5 days a week)

Our client is pioneering a frontier 3D foundation model at the intersection of AI, computer vision, and spatial computing. Their mission is to transform how industries from robotics and AR/VR to gaming and film create and interact with 3D content. The role is to design and maintain scalable infrastructure powering cutting-edge machine learning workloads and production systems in a fast-moving startup environment.

Responsibilities

• Building and maintaining scalable cloud infrastructure (AWS, GCP, Azure) for ML workloads and APIs
• Setting up ML nodes for distributed training and local development
• Managing containerised environments (Docker, Kubernetes, Terraform)
• Optimising storage for big data pipelines supporting ML workloads
• Monitoring systems and responding to incidents, ensuring reliability and performance
• Working closely with ML engineers and researchers to integrate infra with production workloads

What you'll bring

• 6+ years' experience in cloud engineering, ideally with ML-related workloads
• Proficiency in scripting (Bash, PowerShell, Python)
• Start-up/Scale-up Experience
• Strong cloud skills (AWS, GCP, Azure) and containerisation (Docker, Kubernetes)
• Experience in automating deployments and orchestrating cloud environments

Nice to have

• Python (Jupyter, PyTorch), monitoring tools (Prometheus, Grafana), cloud databases (RDS, Aurora, Spanner), CI/CD tools (CircleCI), and data visualisation experience

This is a unique opportunity to join a visionary team redefining AI in 3D, with the chance to make a real impact at the cutting edge of spatial computing.