Job description

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. What You’ll Do - 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 Who You Are Required Qualifications - 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 Internship Information - Paid Internship - May - August (12 weeks) - Tuesday - Friday, 8 hours Equal Employment Opportunity TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law. Reasonable Accommodations TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact accomodations@tensorwave.com. Employment Eligibility All offers of employment are contingent upon verification of identity and authorization to work in the United States, as required by law. Background Checks Where permitted by law, employment may be contingent upon the successful completion of a job-related background check. Data Privacy Notice By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.

Requirements

Must-haves

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.
What You’ll Do
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
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
Internship Information
Paid Internship
May - August (12 weeks)
Tuesday - Friday, 8 hours

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlkubernetes

Platform: ashby
Effort: quick