Meshy
Infrastructure Intern
About this role
Join Meshy's infrastructure team to support a rapidly scaling 3D AI platform handling millions of users and GPU-intensive workloads. You'll work on production cloud infrastructure, Kubernetes systems, and internal tooling while learning platform engineering at a well-funded, industry-leading startup.
What you'll do
- Rebuild and improve staging environments using Terraform and cloud-native tools
- Contribute to infrastructure platform initiatives focused on reliability, automation, and observability
- Support Kubernetes and cloud infrastructure for AI training and inference workloads
- Evaluate and enhance security and compliance processes
- Build internal tooling and automation to improve engineering productivity
- Collaborate with infrastructure and product teams on platform initiatives
What they're looking for
- Python, Go, or backend programming languages
- Linux systems administration
- Kubernetes and Docker
- Terraform and Infrastructure as Code
- Cloud platforms (AWS, GCP, Azure)
- CI/CD pipelines and deployment workflows
- Distributed systems concepts
- DevOps or SRE practices
Benefits
- Hands-on experience with GPU infrastructure and AI systems
- Exposure to real production-impacting engineering challenges
- Work with experienced infrastructure engineers at a leading AI company
- Exposure to platform engineering where SRE and ML systems intersect
- Fast-paced startup environment with strong mentorship opportunities
- Located in Bay Area with global engineering team
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Meshy
Meshy builds a 3D generative AI platform that transforms 3D content creation through advanced AI systems and high-performance graphics technology. The company is hiring infrastructure engineers, fullstack engineers, and graphics specialists to scale its GPU-intensive platform, optimize inference systems, and build production-grade rendering pipelines.
- Website
- meshy.ai
Likely interview questions
- Tell us about a time you worked with Kubernetes, Docker, or Infrastructure as Code (Terraform). What was the project, and what did you learn?
- Meshy runs GPU-intensive AI workloads at scale. How would you approach debugging a performance issue in a Kubernetes cluster serving model inference?