GPU Runtime Systems Engineer for Secure Kubernetes

CoreWeave; Bellevue (WA); In loco; USD 180.000 - 240.000; Tempo pieno

Vantaggi offerti da questo lavoro

Medical, dental, and vision insurance

401(k) with employer match

Tuition Reimbursement

Paid Parental Leave

Flexible PTO

Descrizione del lavoro

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

Day-to-day you will develop GPU-aware sandboxing and scheduling strategies, optimize containerization and I/O performance for latency-sensitive workloads, and influence architectural decisions across Linux

Competenze

• 3+ years of systems or production engineering experience at scale.
• Strong hands-on Kubernetes and cloud-native architectures.
• Experience designing secure sandboxed runtimes.
• Experience with lightweight virtualization (Kata Containers, gVisor, KubeVirt, QEMU).
• Experience GPU-accelerated workloads in multi-tenant environments.
• Proficient in Go, C/C++, Rust, Bash with Linux internals knowledge.
• Ability to diagnose performance and isolation issues across containers, VMs, and infrastructure.

Mansioni

• Design and build secure sandboxed runtime environments for Kubernetes workloads.
• Develop GPU-aware scheduling and isolation for multi-tenant workloads.
• Optimize container, VM, and I/O performance for GPU workloads.
• Profile, benchmark, and tune runtime, virtualization, and GPU stacks.
• Collaborate with security and infrastructure teams to define runtime standards.

Conoscenze

Kubernetes

Container orchestration

Go

C/C++

Rust

Bash

Linux internals

Performance profiling

GPU drivers

Security isolation

Strumenti

Kata Containers

gVisor

KubeVirt

QEMU

Descrizione del lavoro

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

Day-to-day you will develop GPU-aware sandboxing and scheduling strategies, optimize containerization and I/O performance for latency-sensitive workloads, and influence architectural decisions across Linux