GPU Runtime Engineer for Kubernetes & Secure Sandboxes

Showcify; Livingston, New York, Sunnyvale, Bellevue (NJ, NY, CA, WA); On-site; USD 182,000 - 242,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with generous employer match

Flexible PTO

Equity awards

Job summary

CoreWeave is hiring a GPU & Runtime Systems Engineer to design and evolve secure sandboxed runtimes for Kubernetes workloads. You will integrate container runtimes, lightweight VMs, and virtualization to support GPU-accelerated workloads in multi-tenant environments.

You will develop GPU-aware sandboxing and scheduling strategies, optimize I/O and container performance, and influence architecture across Linux, container runtimes, virtualization, and GPU drivers.

Qualifications

• 3+ years of experience in systems, platform, or production engineering at scale.
• Strong hands-on experience with Kubernetes and cloud-native architectures.
• Experience designing, implementing, or operating secure execution environments (container runtimes, sandboxed workloads, or virtualized systems).
• Familiarity with lightweight virtualization and sandboxing technologies (Kata Containers, gVisor, KubeVirt, QEMU).

Responsibilities

• Design and implement secure execution environments for containerized and virtualized workloads.
• Build GPU-aware scheduling, isolation, and resource management strategies for multi-tenant workloads.
• Optimize container, VM, and I/O performance across GPU-accelerated workloads.
• Conduct profiling, benchmarking, and performance tuning for runtime, virtualization, and GPU stacks.
• Contribute to architectural decisions across Linux internals, container runtimes, virtualization layers, and GPU drivers.

Skills

Kubernetes

Container runtimes

Go programming

Linux internals

GPU virtualization

Performance profiling

Tools

Kata Containers

gVisor

KubeVirt

QEMU

Job description

CoreWeave is hiring a GPU & Runtime Systems Engineer to design and evolve secure sandboxed runtimes for Kubernetes workloads. You will integrate container runtimes, lightweight VMs, and virtualization to support GPU-accelerated workloads in multi-tenant environments.

You will develop GPU-aware sandboxing and scheduling strategies, optimize I/O and container performance, and influence architecture across Linux, container runtimes, virtualization, and GPU drivers.