AI Performance Engineer

Cornelis Networks delivers the world’s highest performance scale-out networking solutions for AI and HPC datacenters. Our differentiated architecture integrates hardware, software, and system technologies to maximize the efficiency of GPU, CPU, and accelerator-based clusters at any scale. We’recommitted to innovation, performance, and scalability-helping customers push the boundaries of what’spossible.

We are a fast-growing, forward-thinking team of architects, engineers, and business professionals with a proven track recordof building successful products and companies. As a global organization, our team spans multiple U.S. states and six countries, and we continue to expand with exceptional talent in onsite, hybrid, and fully remote roles.

We’reseeking an AI Performance Engineer. Join us to optimizetraining and multi-node inference across next-gen networking silicon and systems-adapters, switches, and the software stack that ties it all together. You’llpartner with architecture, firmware, software, and lighthouse customers to turn lab results into field-proven wins.

Key Responsibilities

• Measure & optimizeend-to-end performancefor distributed AI workloads (training + multi-node inference) across CPU & GPU clusters and diverse fabrics (Omni-Path, Ethernet, InfiniBand).

• Benchmark, characterize, and tuneopen-source & industry workloads and benchmarks (e.g., Llama, Qwen) on current and future compute, storage, and network hardware, including frameworks such as Megatron-LM, TorchTitan, Composer, vLLM, llama.cpp, etc.

• Design and optimizedistributed serving topologies(sharded/replicated, tensor/pipe parallel, MoEexpert placement), continuous/adaptive batching, KV-cache sharding/offload (CPU/NVMe) & prefix caching.

• Optimizetraining & inferencing: Validate

  RDMA/GPUDirectRDMA, congestion control, and collective/point-to-point tradeoffs during inference.

• Build crisp proof pointsthat compare Cornelis Omni-Path to competing interconnects; translate data into narratives for sales/marketing and existing customers.

• Instrument and visualizeperformance (Nsight Systems, VTune, perf, app timers) plus database visualization (Prometheus/Grafana).

• Evangelize best practicesthrough briefs, READMEs, blogs, articles, and peer-reviewed publications

Minimum Qualification

• BSin CS/EE/CE/Math or related (MS preferred) and 2-3+ yearsrunning AI/ML at cluster scale.

• Proven ability to set up, run, and analyzeAI benchmarks; deep intuition for message passing, collectives, scaling efficiency, and bottleneck huntingfor both training and inference.

• Hands-on with distributed trainingbeyond single-GPU (DP/TP/PP, ZeRO, FSDP, sharded optimizers) and distributed inference architectures(replicated vs sharded, tensor/KV parallel, MoE).

• Practical experience across AI stacks & comms:

  PyTorch, DeepSpeed, Megatron-LM, RCCL/NCCL,vLLM, llama.cpp

• Comfortable with compilers

  (GCC/LLVM/Intel/OneAPI) and MPI

  stacks; Python + shellpower user.

• Familiarity with network architectures(Omni-Path/OPA, InfiniBand, Ethernet/RDMA/RoCE) and Linux

  systems at the performance-tuning level, including NIC offloads, CQ moderation, pacing, ECN.

• Excellent written and verbal communication-turn measurements into

  customer persuasion.

Preferred Qualifications

• Cluster scheduler experience (e.g.SLURM, PBS).

• Hands-on profiling & tracing of GPU/comm paths (Nsight Systems, Nsight Compute, ROCmtools/rocprof/roctracer, VTune, perf, etc.).

• Background in HPC performance engineering or storage (BeeGFS, Lustre, NVMe-oF).

Location

Remote: This role fully supports remote work for employees residing within the United States.

Cornelis is a company that is strongly led by our core values and believes in the responsibility of our people, which is why we fully support remote employees. When safe, we encourage travel for partner and client meetings, conferences, and company in-person collaboration and meetings. Expect travel to be less than 10%.

Cornelis Networks is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.