AI Performance Engineer

Cornelis Networks

. Measure and optimize end-to-end performance for distributed AI workloads, including training and multi-node inference, across CPU/GPU clusters and Omni-Path, Ethernet, and InfiniBand fabrics .

Posted 10/26/2026full-timeRemote • California • United StatesJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in optimizing distributed AI workloads across CPU/GPU clusters, with proficiency in frameworks such as PyTorch and Megatron-LM. Capable of translating complex performance data into actionable insights for stakeholders while ensuring best practices in performance tuning and benchmarking.

Highest-signal resume keywords

Distributed AI Workload OptimizationBenchmarking And Analyzing AI WorkloadsHands-On Distributed Training ExperienceProficiency In PyTorch And DeepSpeedExcellent Written And Verbal Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI BenchmarkingDistributed TrainingPerformance TuningMessage PassingScaling EfficiencyPython ProficiencyShell ScriptingGCC/LLVM/Intel/OneAPI CompilersNIC Offloads KnowledgeHPC Performance Engineering

Soft Skills

Excellent Communication

Tools & Technologies

Nsight SystemsVTunePrometheusGrafanaSLURMPBSRCCL/NCCLROCm ToolsLustreBeeGFS

Industry Keywords

AI/MLDistributed Inference ArchitecturesOmni-PathInfiniBandEthernetRDMALinux Performance TuningCluster ScaleMulti-Node InferencePerformance Engineering

Tech Stack

Tools & technologies

GrafanaLinuxNode.jsPrometheusPythonPyTorch

About the role

Key responsibilities & impact

Measure and optimize end-to-end performance for distributed AI workloads, including training and multi-node inference, across CPU/GPU clusters and Omni-Path, Ethernet, and InfiniBand fabrics
Benchmark, characterize, and tune open-source and industry workloads and benchmarks such as Llama and Qwen on compute, storage, and network hardware
Work with frameworks including Megatron-LM, TorchTitan, Composer, vLLM, and llama.cpp
Design and optimize distributed serving topologies, including sharded/replicated, tensor/pipeline parallel, and MoE expert placement
Optimize continuous/adaptive batching, KV-cache sharding/offload, and prefix caching
Validate RDMA/GPUDirect RDMA, congestion control, and collective/point-to-point tradeoffs during inference
Build proof points comparing Cornelis Omni-Path with competing interconnects
Translate performance data into narratives for sales, marketing, and customers
Instrument and visualize performance using Nsight Systems, VTune, perf, application timers, Prometheus, and Grafana
Evangelize best practices through briefs, READMEs, blogs, articles, and peer-reviewed publications
Partner with architecture, firmware, software, and lighthouse customers to turn lab results into field-proven wins

Requirements

What you’ll need

BS in CS/EE/CE/Math or related (MS preferred)
2–3+ years running AI/ML at cluster scale
Proven ability to set up, run, and analyze AI benchmarks
Deep intuition for message passing, collectives, scaling efficiency, and bottleneck hunting for training and inference
Hands-on distributed training beyond single-GPU (DP/TP/PP, ZeRO, FSDP, sharded optimizers)
Experience with distributed inference architectures (replicated vs sharded, tensor/KV parallel, MoE)
Practical experience with PyTorch, DeepSpeed, Megatron-LM, RCCL/NCCL, vLLM, and llama.cpp
Comfortable with GCC/LLVM/Intel/OneAPI compilers and MPI stacks
Python and shell proficiency
Familiarity with Omni-Path/OPA, InfiniBand, Ethernet/RDMA/RoCE, and Linux performance tuning
Knowledge of NIC offloads, CQ moderation, pacing, and ECN
Excellent written and verbal communication
Preferred: Cluster scheduler experience (SLURM, PBS)
Preferred: GPU/communications profiling and tracing with Nsight Systems, Nsight Compute, ROCm tools/rocprof/roctracer, VTune, or perf
Preferred: HPC performance engineering or storage background (BeeGFS, Lustre, NVMe-oF)

Benefits

Comp & perks

Full-Time (U.S. Benefit Eligible)
Fully supported remote work for employees residing within the United States
Travel to partner and client meetings, conferences, and company in-person collaboration and meetings (less than 10%)