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.