Senior Network Production Engineer, AI Supercomputing

Own Production Network Health

• Own the availability, performance, and operational readiness of the MRC Ethernet backend network.
• Define and operate service-level indicators for packet loss, congestion, link health, path diversity, bandwidth, tail latency, collective performance, and job impact.
• Detect slow or degraded components before they cause training failures or reduce model FLOPs utilization.
• Build health models that correlate switch, NIC, host, topology, and application telemetry.

Operate the Network at Frontier Scale

• Participate in on-call rotation and lead the response to high-severity network incidents.
• Diagnose failures spanning GPUs, NICs, switches, cables, firmware, drivers, network operating systems, MRC, NCCL, Kubernetes, and training workloads.
• Develop automated mitigation mechanisms, including path avoidance, node quarantine, workload relocation, and safe component remediation.
• Create operational procedures for maintenance, upgrades, rollback, capacity expansion, and topology changes.

Improve Large-Job Reliability and Performance

• Work directly with training teams to investigate collective-performance degradation, stalls, timeouts, job restarts, and unexplained MFU loss.
• Translate low-level network signals into clear job-level impact.
• Establish network qualification gates for admitting nodes and racks into large training pools.
• Run failure-injection and resilience testing to validate behavior under link, NIC, switch, and path failures.
• Improve placement and routing policies for jobs spanning racks, rows, and network planes.

Automate Fleet Operations

• Build production-quality software and automation for network validation, monitoring, diagnosis, remediation, and fleet-wide change management.
• Replace manual investigation with deterministic workflows and actionable alerts.
• Automate firmware, driver, configuration, and network operating-system compliance checks.
• Maintain authoritative network inventory, topology, and configuration state.
• Reduce mean time to detect, isolate, mitigate, and permanently resolve failures.

Drive Cross-Company Execution

• Lead technical investigations involving internal infrastructure teams, cloud and datacenter operators, Azure Networking, NVIDIA, switch and NIC teams, and network-software partners.
• Own incidents through resolution, even when the underlying failure crosses organizational boundaries.
• Produce clear root-cause analyses with corrective actions, owners, and completion dates.
• Convert recurring production failures into requirements for future network, server, and accelerator architectures.
• Create runbooks and train engineers across regions to provide reliable round-the-clock coverage.

• Bachelor’s Degree in Computer Science, or related technical discipline AND 4+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering
• OR equivalent experience
• Experienceoperatinglarge-scaledatacenterorhigh-performancecomputingnetworksinproductionenvironments.
• ExperiencewithEthernet,RDMA,RoCEv2,congestioncontrol,routing,loadbalancing,andlosslessornear-losslessnetworkdesign.
• Experiencediagnosingfailuresacrossswitches,NICs,hosts,drivers,firmware,anddistributedapplications.
• ExperienceworkingwithLinuxsystemsandprogramminginPython,Go,C++,oranothersystems-orientedprogramminglanguage., * Master’s Degree in Computer Science, or related technical discipline AND 2+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering
• OR equivalent experience
• Experience operating GPU training clusters or other tightly coupled distributed-computing systems.
• Knowledge of MRC or other multi-rail, multi-plane, multipath Ethernet transports.
• Experience with NCCL, collective communications, CUDA, GPUDirect RDMA, and distributed training frameworks.
• Familiarity with ConnectX-class NICs, modern Ethernet switch ASICs, SONiC, SAI, and switch telemetry.
• Experience with InfiniBand and the operational differences between InfiniBand and Ethernet-based AI fabrics.
• Familiarity with Kubernetes, Slurm, workload placement, and cluster-health certification.
• Experience with streaming telemetry, gNMI, Prometheus, Datadog, Kusto, eBPF, or equivalent observability systems.
• Experience managing fleet-wide firmware, driver, or network operating-system changes.
• A track record of improving reliability through automation rather than recurring manual intervention.

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.