Senior Software Engineer

Diagnose and resolve communication-related performance bottlenecks across large-scale distributed systems. Analyze InfiniBand fabric behavior, including congestion, credit starvation, route imbalance, or link-level errors. Tune and debug Remote Direct Memory Access (RDMA) transport layers, message ordering, path migration, and Quality of Service (QoS) policies on multi-rail or multi-subnet fabrics. Develop custom tooling or scripts to analyze fabric telemetry, traffic flows, and job-induced network behavior. Collaborate with operations team to ensure optimal use of fabric features (adaptive routing, congestion control, service level tuning). Work closely with hardware vendors (e.g., NVIDIA) to evaluate and validate next-gen interconnects and switch firmware. Support benchmarking of High-Performance Computing (HPC) / Artificial Intelligence (AI) workloads, including characterization of latency, bandwidth, and collective efficiency under scale. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Proven history of debugging network performance issues at scale (e.g., link-level failures, fabric hot spots, routing asymmetry). Expert-level experience with Message Passing Interface (MPI), NVIDIA Collective Communications Library (NCCL), and collective algorithm behavior in multi-node, multi-GPU systems Deep knowledge of InfiniBand architecture, including verbs, Remote Direct Memory Access (RDMA), Scalable Hierarchical Aggregation Protocol (SHARP), adaptive routing, congestion control, and Quality of Service (QoS). Familiarity with job schedulers and network-aware scheduling/topology constraints. Familiarity with GPU communication stack internals: GPU Direct RDMA and NVLink topologies.

with real-world performance tuning of transformer training, large-scale AllReduce, or model/data parallel Artificial Intelligence (AI) workloads.