Network Engineer, Supercomputing

What You’ll Do

• Reason about and validate GPU network fabric design across our deployments.
• Debug RDMA / RoCEv2 across different NIC vendors. Diagnose collective failures of production NCCL, PFC/ECN tuning, and congestion control behavior.
• Own NVLink / NVSwitch interconnect — including fabric manager and IMEX health, link and lane errors, and how the GPU fabric interacts with collectives.
• Build host-level network instrumentation and use Linux tooling to build dashboards and alerts, not just the bug report.
• Navigate cross-cloud fabric quirks across providers and triage across the NIC, driver, kernel, switch, and workload boundaries.
• Drive escalations with cloud-provider networking teams, owning issues end-to-end until they're resolved.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Experience operating large‑scale clusters and container orchestration systems (e.g. Kubernetes or Slurm).
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications:

• Fluency with host-level debugging tools on Linux.
• Strong communication skills, internally and with cloud providers.
• Extensive experience with at least one of the following:
  • Familiarity with cloud network primitives across at least two cloud providers.
  • Hands-on experience with NVLink / NVSwitch, fabric manager, and IMEX.
  • Statistical rigor in reliability reasoning — comfort reasoning about failure and error rates, distributions, and base rates, and the judgment to separate signal from noise when characterizing a large fabric.
  • A track record of writing tooling that made the next debugging session meaningfully faster.
  • Familiarity with CUDA/NCCL and performance profiling for distributed training and inference.
  • Understanding of deep learning frameworks and their underlying system architectures.

Compensation and Benefits

• Compensation: $350,000 - $475,000 USD annual salary range.
• Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
• Visa sponsorship available.