Software Engineering Intern

About TensorWave

Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure.

About the Role

We’re looking for a Software Engineering Intern to join our team during an exciting phase of growth. In this role, you'll join a small, high-impact engineering team building the systems that power the future of AI compute. You'll work on real production challenges in a cutting-edge GPU cloud environment, gaining hands-on experience with bare-metal infrastructure, large-scale distributed systems, and AMD's ROCm ecosystem.

This is an excellent opportunity for students passionate about cloud infrastructure, AI/ML systems, performance optimization, and contributing to tools that AI innovators use every day.

Internship Information

- Paid Internship
- May - August (12 weeks)
- Tuesday - Friday, 8 hours

Employment Eligibility

All offers of employment are contingent upon verification of identity and authorization to work in the United States, as required by law.

Responsibilities

Collaborate with senior engineers on features for our cloud control plane, orchestration layer, user-facing APIs, or internal tooling; Work on automation, monitoring, and observability for GPU clusters (Slurm + Kubernetes-native environments); Participate in debugging performance bottlenecks in high-throughput inference or distributed training pipelines; Write clean, well-tested code and participate in code reviews; Learn how bare-metal AI clouds operate at scale—from hardware partitioning to high-speed networking and storage

Requirements

Collaborate with senior engineers on features for our cloud control plane, orchestration layer, user-facing APIs, or internal tooling; Work on automation, monitoring, and observability for GPU clusters (Slurm + Kubernetes-native environments); Participate in debugging performance bottlenecks in high-throughput inference or distributed training pipelines; Write clean, well-tested code and participate in code reviews; Learn how bare-metal AI clouds operate at scale—from hardware partitioning to high-speed networking and storage