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’re committed to innovation, performance, and scalability—helping customers push the boundaries ofwhat’s possible. 

We are a fast-growing, forward-thinking team of architects, engineers, and business professionals with a proventrack record of 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’re seeking anAI Performance Engineer. Join us tooptimize training and multi-node inference across next-gen networking silicon and systems—adapters, switches, and the software stack that ties it all together.You’ll partner with architecture, firmware, software, and lighthouse customers to turn lab results into field-proven wins.

Key Responsibilities

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

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

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

• Optimize training & inferencing:  Validate
  RDMA/GPUDirect RDMA, congestion control, and collective/point-to-point tradeoffs during inference.

• Build crisp proof points that compare Cornelis Omni-Path to competing interconnects; translate data into narratives for sales/marketing andexistingcustomers.

• Instrument and visualize performance (Nsight Systems,VTune, perf, app timers) plus databasevisualization  (Prometheus/Grafana).

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

Minimum Qualification

• BS in CS/EE/CE/Math or related (MS preferred) and 2-3+ years running AI/ML at cluster scale.

• Proven ability toset up, run, and analyze AI benchmarks; deep intuition formessage passing, collectives, scaling efficiency, and bottleneck hunting for both training andinference.

• Hands-on withdistributed training beyond single-GPU (DP/TP/PP,ZeRO, FSDP, sharded optimizers) anddistributed inference architectures (replicated vs sharded, tensor/KV parallel,MoE).

• Practical experience acrossAI stacks & comms:
  PyTorch,DeepSpeed, Megatron-LM,RCCL/NCCL, vLLM, llama.cpp

• Comfortable withcompilers
  (GCC/LLVM/Intel/OneAPI) andMPI
  stacks;Python + shell power user.

• Familiarity withnetwork architectures (Omni-Path/OPA, InfiniBand, Ethernet/RDMA/RoCE) andLinux
  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,ROCm tools/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.