Senior Distributed Systems Engineer

About the Institute of Foundation Models The Institute of Foundation Models (IFM) designs and operates ultra-scale GPU supercomputing systems to train next-generation foundation models. We believe performance, fault tolerance, and scalability are co-designed across model architecture, communication systems, runtime, and hardware topology. This role sits at the core of that effort — driving communication performance, distributed reliability, and cross-layer optimization for large-scale training workloads.   The Mission We are looking for a deeply technical engineer to co-design and optimize the communication stack for large-scale distributed training, including hybrid parallelism and Mixture-of-Experts (MoE) workloads. This is not a network operations role. This is a systems-level engineering position focused on performance engineering, distributed debugging, and communication-runtime co-design. • Design and optimize expert-parallel and hybrid-parallel communication patterns • Drive high-performance hierarchical collectives for MoE workloads • Co-design runtime orchestration with communication topology awareness • Reduce tail latency and improve determinism across thousands of GPUs • Architect fault-tolerant distributed execution under real-world cluster failures Core Technical Scope • Communication-compute overlap and topology-aware collective optimization • Deep debugging of NCCL, RDMA, and custom communication layers • Hybrid expert parallel strategies in modern large-scale MoE systems • Elastic and resilient distributed job orchestration concepts • Congestion analysis and routing optimization across InfiniBand/RoCE fabrics • Microbenchmarking and performance modeling for communication-heavy workloads Expected Technical Depth • Hybrid expert parallel communication for Mixture-of-Experts training • Scaling behavior under network pressure • Distributed orchestration for elastic, large-scale training • Fault detection and recovery in distributed GPU workloads • Cross-layer bottlenecks: GPU ↔ NIC ↔ PCIe ↔ NVSwitch ↔ Fabric ↔ Scheduler Required Background • Experience optimizing distributed training at 1,000+ GPU scale (or equivalent depth) • Hands-on expertise with RDMA, InfiniBand, RoCE, and GPUDirect RDMA • Deep familiarity with NCCL and/or UCX internals • Strong systems programming ability (C/C++, Rust, or Go) • Strong familiarity with modern model training frameworks such as PyTorch • Ability to troubleshoot and profile training performance issues related to communication bottlenecks • Ability to translate research ideas into production-grade optimizations • Experience debugging distributed hangs, desynchronization, and performance regressions What We Mean by "Hardcore" • You can explain why an communication degrades at scale and how to fix it • You have improved real cluster throughput via communication redesign • You can trace a distributed hang across ranks and identify the root cause • You are comfortable working at the boundary between hardware and runtime Application Requirements • Include a link to your GitHub (required) • Provide links to relevant distributed systems, HPC, or large-scale training projects • Include a list of publications and/or public technical reports (if applicable) • Describe the hardest distributed debugging problem you solved • Include measurable performance improvements you have delivered Academic Qualifications Master’s, or Bachelor’s + 1 year of relevant experience.