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BasetenBasetenSan Francisco, CA

Software Engineer — GPU Networking & Distributed Systems

Software engineer architecting GPU networking software with RDMA for distributed AI inference. Optimizes communication for H100/Blackwell clusters, WideEP, and serverless LLM startups using C++/Python and NVIDIA tools.

150k – 250k/yr
On-siteBackend Engineering

About the role

What You'll Do

  • Make RDMA First-Class: Integrate RDMA/RoCE/InfiniBand into inference stack for bandwidth/latency improvements.
  • Optimize Distributed Inference: Implement networking for Disaggregated KV Cache Offload and WideEP across NVLink/InfiniBand.
  • Enable Serverless-Grade Startup Speeds: Work on checkpointing/storage for sub-10s startup of trillion-parameter models.
  • Deep-Dive into Hardware: Characterize H100/H200, B200/B300, GB200/300 NVL72 clusters with acceptance tests.
  • Build Observability: Design tools for packet flow, congestion, bandwidth visualization.
  • Optimize Kernels: Use NCCL, NVSHMEM; write custom communication kernels.

Who You Are

  • Deep experience with high-performance networking (InfiniBand, RoCE v2).
  • Fluent in C++ or Python, understanding NVIDIA memory hierarchy (H100/Blackwell).
  • Comfortable diving into TensorRT-LLM source, custom bindings, NVLink debugging.
  • Know when to use/build custom solutions beyond standard tools.

Highly Preferred

  • Deep knowledge of NCCL, NVSHMEM, UCX.
  • Experience with GPUDirect Storage (GDS), Weka/3FS.
  • Familiarity with TensorRT-LLM, vLLM, Sglang.
  • Running low-level benchmarks for hardware qualification.

Benefits

  • Competitive compensation with equity.
  • 100% medical/dental/vision coverage.
  • Generous PTO, Winter Break.
  • Paid parental leave, 401(k).

Skills

RdmaRoceInfiniBandNcclNvshmemUcxC++PythonTensorrt-LlmNvlink

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