Infrastructure Intern

About Meshy

Headquartered in Silicon Valley
, Meshy is the leading 3D generative AI company on a mission to Unleash 3D Creativity by transforming the content creation pipeline. Meshy makes it effortless for both professional artists and hobbyists to create unique 3D assets—turning text and images into stunning 3D models in just minutes. What once took weeks and cost $1,000 now takes just 2 minutes and $1.

Our world‑class team of top experts in computer graphics, AI, and art includes alumni from MIT, Stanford, and Berkeley, as well as veterans from Nvidia and Microsoft. Our talent spans the globe, with team members distributed across North America, Asia, and Oceania
, fostering a diverse and innovative multi‑regional culture focused on solving global 3D challenges. Meshy is trusted by top developers, backed by premiere venture capital firms like Sequoia and GGV
, and has successfully raised $52 Million in funding.

Meshy is the market leader, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games) and No.1 in website traffic (according to Similar Web, with 3 Million monthly visits). The platform boasts over 5 Million users and has generated 40 Million models
.

Founder and CEO Yuanming (Ethan) Hu earned his Ph.D. in graphics and AI from MIT, where he developed the acclaimed Taichi GPU programming language (27K stars on Git Hub, used by 300+ institutes). His work is highly influential, including an honorable mention for the SIGGRAPH 2022 Outstanding Doctoral Dissertation Award and over 2,700 research citations.

About the Role:

• This internship sits at the intersection of platform engineering, cloud infrastructure, security, and AI systems. You will work directly with experienced infrastructure engineers on projects that support Meshy’s rapidly growing AI platform and GPU‑based workloads.

• This is a hands‑on engineering internship where you will contribute to real production‑impacting infrastructure initiatives across cloud platforms, developer tooling, observability, automation, and internal platform systems.

You’ll gain exposure to:

• GPU infrastructure and AI platform engineering
• Kubernetes and cloud‑native systems
• Infrastructure as Code (Terraform)
• CI/CD and deployment workflows
• Security and compliance tooling
• Reliability and scalability challenges in AI systems

What Projects You’ll Work On:

• Rebuilding and improving staging environments in non‑production infrastructure using Terraform and cloud‑native tooling
• Contributing to internal infrastructure platform initiatives across reliability, automation, observability, and developer experience
• Supporting Kubernetes‑and‑cloud‑based infrastructure used for AI training and inference workloads
• Helping evaluate and improve security and compliance tooling and processes
• Building internal tooling and automation to improve engineering productivity and operational efficiency
• Collaborating with infrastructure, research, and product engineering teams on high‑priority platform initiatives

What Makes the Work Interesting:

• GPU training clusters using Kubernetes and distributed systems concepts
• AI inference infrastructure serving multimodal generative models at scale
• Real‑world platform engineering challenges involving performance, reliability, and cost optimization
• Opportunity to work where platform engineering, SRE, and applied ML systems meet in practice

Qualifications:

• Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Software Engineering, or a related technical field
• Strong coding skills in Python, Go, or similar backend languages
• Familiarity with Linux systems and cloud platforms (AWS, GCP, or Azure)
• Exposure to Kubernetes, Docker, Terraform, or Infrastructure as Code concepts
• Interest in infrastructure engineering, distributed systems, Dev Ops, SRE, or AI infrastructure
• Strong curiosity, ownership mindset, and willingness to learn quickly in a fast‑paced startup environment

Nice to Have:

• Experience with personal projects, hackathons, open‑source contributions, or research projects
• Familiarity with CI/CD pipelines, observability tooling, or cloud security concepts
• Exposure to GPU infrastructure, ML systems, or AI platforms
• Experience deploying…