Senior Software Engineer, Sandboxes & Virtualization

Weights & Biases Weights & Biases · Data AI · Bellevue, WA +4 · Technology

This role focuses on building and operating secure, high-performance environments for multi-tenant Kubernetes platforms at scale, specifically for AI-driven and GPU-accelerated workloads. The engineer will design, implement, and evolve sandboxed runtime environments, focusing on runtime isolation, performance, and security, integrating container runtimes, lightweight VMs, and virtualization technologies.

What you'd actually do

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

Skills

Required

  • 3+ years of experience in systems, platform, infrastructure, or production engineering at scale.
  • Strong hands-on experience with Kubernetes, container orchestration, and cloud-native architectures, including controllers, operators, or scheduling extensions.
  • Experience designing, implementing, or operating secure execution environments (container runtimes, sandboxed workloads, or virtualized systems).
  • Practical experience with lightweight virtualization and sandboxing technologies (e.g., Kata Containers, gVisor, KubeVirt, QEMU).
  • Experience supporting GPU-accelerated workloads in multi-tenant environments, including GPU scheduling, isolation, device passthrough, mediated devices, or virtualization.
  • Proficient in systems-oriented programming (Go, C/C++, Rust, Bash) with strong Linux internals knowledge.
  • Skilled at diagnosing and resolving complex performance, reliability, or isolation issues across containers, VMs, and infrastructure.
  • Experienced in profiling, benchmarking, and tuning performance across runtime, virtualization, and GPU stacks.

Nice to have

  • Experience building systems for safely executing untrusted or sensitive workloads in shared environments.
  • Familiarity with GPU drivers and low-level virtualization or I/O optimization techniques.
  • Experience defining threat models and implementing runtime security policies in multi-tenant systems.

What the JD emphasized

  • secure sandboxed runtime environments
  • GPU-accelerated workloads
  • multi-tenant environments
  • runtime isolation
  • performance
  • security
  • container runtimes
  • lightweight VMs
  • virtualization technologies
  • GPU drivers