Job description

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 of what’s possible. We are a fast-growing, forward-thinking team of architects, engineers, and business professionals with a proven track 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 an AI Performance Engineer . Join us to optimize 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 future compute , storage, and network hardware, including frameworks such as Megatron-LM, TorchTitan , Composer, vLLM , llama.cpp, etc. Design and optimize 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 and existing customers . Instrument and visualize performance (Nsight Systems, VTune , perf, app timers) plus database visualization ( 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 to set up, run, and analyze AI benchmarks; deep intuition for message passing, collectives, scaling efficiency, and bottleneck hunting for both training and inference . Hands-on with distributed training beyond 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 + shell power 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, ROCm tools/ rocprof / roct racer , 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.

Requirements

Must-haves

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).
Design and optimize 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 and existing customers .
Instrument and visualize performance (Nsight Systems, VTune , perf, app timers) plus database visualization ( Prometheus/Grafana).
Evangelize best practices through briefs, READMEs, blogs, articles, and peer-reviewed publications
Proven ability to set up, run, and analyze AI benchmarks; deep intuition for message passing, collectives, scaling efficiency, and bottleneck hunting for both training and inference .
Hands-on with distributed training beyond 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 + shell power 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.

Nice-to-haves

BS in CS/EE/CE/Math or related (MS preferred) and 2 -3 + years running AI/ML at cluster scale.
Cluster scheduler experience ( e.g. SLURM, PBS).
Hands-on profiling & tracing of GPU/comm paths (Nsight Systems, Nsight Compute, ROCm tools/ rocprof / roct racer , VTune , perf, etc.).
Background in HPC performance engineering or storage ( BeeGFS , Lustre , NVMe-oF ).
Remote: This role fully supports remote work for employees residing within the United States.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpython

Platform: bamboohr
Effort: medium