Member of Technical Staff - ML Infrastructure Engineer (AI)

TL;DR

Member of Technical Staff - ML Infrastructure Engineer (AI/MLOps): Designing and maintaining the cloud-based infrastructure for frontier AI research with an accent on training and inference clusters, network-based storage, and IaC. Focus on optimizing GPU resource allocation, reducing training bottlenecks, and building scalable CI/CD pipelines for generative models.

Location: Must be based in or be able to commute to Freiburg (Germany) or San Francisco (USA) for hybrid work (2 days/week) or remote work with a mandatory monthly in-person week.

Salary: $180,000–$300,000 USD

Company

hirify.global is a research lab creating foundational generative models, including FLUX, used by millions of creators and developers worldwide.

What you will do

• Design, deploy, and maintain cloud-based ML training clusters (Slurm) and inference clusters (Kubernetes).
• Manage network-based cloud file systems and S3 storage optimized for large-scale ML workloads.
• Develop and maintain Infrastructure as Code (IaC) using Terraform and Ansible to prevent configuration drift.
• Implement and optimize CI/CD pipelines for ML workflows to accelerate the path from experiment to production.
• Design custom autoscaling solutions for ML workloads and ensure security best practices across the stack.
• Build developer-friendly tools and practices to make ML operations efficient for researchers.

Requirements

• Strong proficiency in cloud platforms (AWS, Azure, or GCP) focusing on AI/ML services.
• Extensive production experience with Kubernetes and Slurm cluster management.
• Expertise in Infrastructure as Code tools (Terraform, Ansible, etc.).
• Proven track record managing and optimizing network-based cloud file systems and object storage for ML.
• Experience with CI/CD tools such as CircleCI, GitHub Actions, or ArgoCD in ML contexts.
• Requirement to join the team in Freiburg or SF at least 2 days a week, or work remotely with a monthly in-person week.

Nice to have

• Experience building custom autoscaling solutions for ML workloads.
• Knowledge of cost optimization strategies for cloud-based GPU infrastructure.
• Familiarity with MLOps practices, HPC environments, and data versioning.
• Knowledge of network optimization techniques for distributed ML training.

Culture & Benefits

• Work in a frontier research lab focused on research excellence and open science.
• Low-ego culture where the best idea wins and credit is shared.
• Bold approach to shipping and taking ambitious technical bets.
• Company covers reasonable travel costs for mandatory in-person connection weeks.
• High-impact environment within a small team (~50 people) pushing the edge of generative AI.