Design, deploy, and optimize multi-node GPU clusters for AI training and inference workloads.
Tune distributed training environments to maximize GPU utilization, throughput, and scaling efficiency.
Optimize inference clusters for maximum token generation throughput, low latency, and high GPU utilization.
Build and support production AI infrastructure running hundreds to thousands of GPUs.
Analyze and eliminate performance bottlenecks across compute, networking, storage, and software layers.
Perform NCCL benchmarking, analysis, and tuning to achieve optimal collective communication performance.
Design and optimize GPU networking using InfiniBand or RoCE v2, including RDMA, congestion management, topology awareness, and QoS.
Configure and tune distributed AI software stacks including:
PyTorch
NCCL
CUDA
UCX
MPI
Slurm
Pyxis/Enroot
Optimize GPU scheduling and resource allocation for both training and inference environments.
Develop repeatable benchmarking and validation processes for new hardware, firmware, drivers, and software releases.
Identify performance regressions and troubleshoot distributed training issues at scale.
Optimize storage architectures for AI workloads, including checkpointing, dataset streaming, and high-performance parallel I/O.
Work closely with ML engineers to improve training scalability and inference efficiency.
Create automation to deploy, validate, benchmark, and monitor GPU clusters.
Evaluate emerging AI infrastructure technologies and recommend improvements to platform architecture.
Required Qualifications
7+ years designing or operating large-scale Linux infrastructure.
5+ years supporting production GPU clusters for AI or HPC workloads.
Demonstrated experience building multi-node GPU training environments from the ground up.
Deep expertise with distributed PyTorch training.
Extensive experience troubleshooting and optimizing NCCL communications.
Strong understanding of distributed AI communication patterns, including:
Experience benchmarking distributed training using tools such as:
nccl-tests
NVIDIA DCGM
Nsight Systems
MLPerf (preferred)
Strong understanding of GPU memory management, including:
KV Cache
Activation checkpointing
Tensor Parallelism
Pipeline Parallelism
Data Parallelism
Experience optimizing LLM inference throughput, including:
Experience tuning CUDA, NCCL, UCX, and MPI for maximum distributed performance.
Expert-level Linux systems administration skills.
Experience with Slurm workload manager.
Experience using Pyxis and Enroot for containerized GPU workloads.
Strong scripting skills using Python and Bash.
Experience deploying AI workloads on Kubernetes.
Experience with NVIDIA GPU Operator.
Experience with Kubernetes batch scheduling (Volcano, Kueue, Run:ai, etc.).
Experience with distributed inference platforms such as vLLM, TensorRT-LLM, or SGLang.
Experience with NVIDIA DGX SuperPOD or similar large-scale GPU deployments.
Familiarity with MLPerf benchmarking.
Experience deploying monitoring solutions such as Prometheus, Grafana, and DCGM Exporter.
Experience automating infrastructure using Ansible, Terraform, or similar tools.
Experience working in cloud GPU environments (AWS, Azure, GCP) in addition to bare metal.
