Infrastructure Engineer

Infrastructure Engineer

Our client is the leading 3D scanning company in the world. They leverage state-of-the-art in Computer Vision and Machine Learning to scan highest quality, relightable 3D models of objects and products at scale.
 

Their 3D scanners are used globally by customers such as Meta, adidas, NUREG, Inditex (ZARA, Massimo Dutti, etc.), Gucci, Salomon, Quad, and other leading brands, content creation studios, and technology companies to mass produce tens of thousands of 3D assets per year.
 

The international team of engineers is continuously looking to complement the team with the best talents that want to push the company to the next level. If you are results-driven, brilliant and want to join an ambitious company at an early stage, then this is for you.
 

Position Overview

We’re looking for an experienced Infrastructure Engineer to improve and scale our technical infrastructure for deployment, training pipelines, resource management, and workflow orchestration. You’ll support our development and ML teams across cloud and on-premise environments. If you have experience in several of these areas and are excited about growing in others, we encourage you to apply.
 

Main Responsibilities:

>DevOps & Deployment

• Design and implement deployment strategies for software and assets to cloud environments and clients
• Create reliable pipelines for delivering and updating various resources to on-premise systems
• Manage multi-environment infrastructure (development, staging, production)
• Build and maintain CI/CD pipelines for efficient software delivery
• Design testing infrastructure to ensure software quality
• Improve developer experience through tooling and workflow optimization

>Compute & ML Infrastructure

• Manage hardware compute clusters and implement container orchestration systems for development, experimentation, and diverse workloads
• Provision, configure, and optimize heterogeneous resources across distributed systems for parallel computing tasks
• Build and maintain infrastructure for ML workloads, including training pipelines and data processing
• Design and optimize resource allocation systems for efficient AI/ML computation

Qualifications:

• Bachelor’s degree in Computer Science or related technical field
• 4+ years of experience in infrastructure engineering or DevOps roles
• Proficiency in English
• Experience with containerization (Docker) and orchestration (e.g. Kubernetes)
• Knowledge of infrastructure as code tools (Terraform, Ansible) and CI/CD pipelines
• Experience with HPC clusters (GPU+CPU) and job scheduling systems (e.g. Slurm)
• MLOps experience, including ML pipeline automation and experiment tracking

Nice to have:

• Experience with monitoring solutions (Prometheus, Grafana)
• Familiarity with cloud services, particularly AWS
• Understanding of networking principles and implementation
• Familiarity with Hardware infrastructure management (IPMI, server maintenance)
• Knowledge of 3D graphics processing or computer vision workflows