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 Academic Qualifications Master’s, or Bachelor’s + 1 year of relevant experience. Visa Sponsorship This position is eligible for visa sponsorship. Benefits Include Comprehensive medical, dental, and vision benefits Bonus 401K Plan Generous paid time off, sick leave and holidays Paid Parental Leave Employee Assistance Program Life insurance and disability We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr