TEN openings here!  

Please focus on candidates with deep hands-on Kubernetes infrastructure experience at significant scale. Strong profiles will also demonstrate Python automation, Terraform, GPU/HPC infrastructure, NVIDIA GPU exposure, and production troubleshooting experience.

Role Overview

We are seeking a highly experienced High Performance Computing (HPC) / Kubernetes Infrastructure Engineer to build, operate, troubleshoot, and scale the infrastructure supporting large-scale AI and LLM workloads.

This is a deeply hands-on engineering role focused on the infrastructure underneath AI models, including Kubernetes, GPU compute, automation, cloud/on-prem infrastructure, job scheduling, monitoring, and large-scale distributed environments.

Candidates should have experience working in complex, production-scale infrastructure environments, ideally connecting hundreds or thousands of servers into cohesive compute platforms.

Key Responsibilities

• Build, operate, troubleshoot, and scale large-scale Kubernetes environments supporting AI and compute-intensive workloads.
• Design and support infrastructure utilizing NVIDIA GPUs and GPU-based compute environments.
• Develop Python tooling and automation to integrate and manage infrastructure components.
• Build and maintain infrastructure using Terraform.
• Support GPU/HPC workload scheduling and large-scale compute environments.
• Implement monitoring, troubleshooting, and incident response across complex infrastructure.
• Work with AWS infrastructure, including EC2, S3, EFS, and FSx.
• Support infrastructure across cloud and on-prem environments.
• Work with multi-cloud Kubernetes environments, including platforms such as GCP and CoreWeave.
• Take ownership of complex infrastructure problems and drive solutions with minimal oversight.

Required Qualifications

• 4+ years of hands-on Kubernetes experience at scale.
• Strong experience building and operating large, complex infrastructure environments.
• Strong Python programming experience, particularly for infrastructure tooling and automation.
• Hands-on experience with Terraform / Infrastructure as Code.
• Experience with GPU infrastructure, HPC environments, or GPU/HPC job scheduling.
• Strong production monitoring, troubleshooting, and incident response experience.
• Experience with AWS, particularly EC2, S3, EFS, and/or FSx.
• Demonstrated ability to independently solve complex infrastructure and systems problems.
• Must be a hands-on engineer who has actually built, operated, scaled, and troubleshot production infrastructure.

Highly Preferred

• Direct High Performance Computing (HPC) experience.
• Experience supporting AI, machine learning, or LLM infrastructure.
• Hands-on experience with NVIDIA GPU infrastructure.
• Experience operating environments consisting of hundreds to thousands of servers.
• Multi-cloud Kubernetes experience, particularly GCP and/or CoreWeave.
• Experience with both on-prem and cloud infrastructure.

Ideal Candidate

The ideal candidate comes from a strong HPC, Kubernetes Platform Engineering, AI Infrastructure, GPU Infrastructure, or large-scale distributed systems background. We are looking for engineers who thrive in technically demanding environments, take ownership, and can execute independently without significant hand-holding.

This is not primarily an AI/ML model development role. The focus is on building the infrastructure and systems that enable AI and LLM workloads to operate at scale.

Technical Priority Areas

1. Kubernetes at scale, 4+ years: Highest priority
2. Python tooling & automation
3. Terraform
4. GPU / HPC job scheduling
5. Monitoring & incident response
6. AWS: EC2, S3, EFS, FSx
7. Multi-cloud Kubernetes: GCP / CoreWeave