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AI Performance Engineer

Cornelis Networks
San Jose, USA
Junior · 2+ years experience
Onsite
Discovered 1 weeks ago
linuxpythonpytorch

Full Job Posting

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

Qualifications

  • 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).
  • 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.

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