Software Engineer — GPU Networking & Distributed Systems

ABOUT BASETEN

Baseten powers mission‑critical inference for the world's most dynamic AI companies, like Cursor, Notion, Open Evidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting‑edge models into production. We're growing quickly and recently raised our $300M Series E, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction.

Join us and help build the platform engineers turn to to ship AI products.

At Baseten, we are building the global operating system for distributed, heterogeneous AI hardware. We believe that as LLM and multi‑modal workloads scale, the network is the computer. We are looking for foundational engineers to lead our GPU Networking efforts, making RDMA a first‑class building block in our infrastructure and unlocking the next generation of distributed inference optimizations.

Networking and compute are no longer separate disciplines; they are converging. The massive throughput of H100, B200, and NVL
72 architectures enables and demands a new approach where communication is co‑optimized alongside computation. We are entering an era where the network is an active accelerator, leveraging smart hardware offloads and direct interconnects to ensure that data movement operates at wire‑speed.

In this role, you will go beyond network configuration to architect the software fabric that unifies thousands of GPUs into a cohesive operating system. While you will leverage the best of the open‑source ecosystem, you won't be limited by it. Where off‑the‑shelf solutions stop, you will build from scratch, engineering the primitives required to co‑optimize communication and compute for Disaggregated Serving, Wide Expert Parallelism (WideEP), and lightening cold starts.

• Make RDMA First‑Class: You will work on integrating RDMA/RoCE/Infini Band capabilities directly into our inference stack, helping us move beyond TCP/IP to unlock order‑of‑ magnitude improvements in bandwidth and latency.

• Optimize Distributed Inference: You will implement and tune the networking layers necessary for efficient Disaggregated KV Cache Offload and WideEP
  , ensuring seamless communication across NVLink and Infini Band for our MoE models.

• Enable Serverless‑Grade Startup Speeds for LLMs: You will work deeply with checkpointing and storage mechanisms to enable sub‑10‑second startup for trillion‑parameter models.

• Deep‑Dive into Hardware: You will characterize and validate networking performance on bleeding‑edge clusters (H100/H200, B200/B300, GB200/300 NVL
  72), writing the acceptance tests that ensure our hardware delivers peak achievable throughput and minimal latency.

• Build Observability: You will design the tools that let us visualize packet flow, congestion, and effective bandwidth across the GPU interconnects, helping us diagnose complex distributed system behaviors.

• Optimize Kernels: You will work with communication libraries (NCCL, NVSHMEM) and potentially write custom communication kernels to overlap compute and data transfer.

• You have deep experience with high‑performance networking protocols (Infini Band, RoCE v2) and understand the physics of data movement.

• You are fluent in C++ or Python, with the ability to bridge the gap between high‑level logic and hardware. You have a deep understanding of the memory hierarchy in modern NVIDIA architectures (H100/Blackwell) and know how to optimize for it.

• You like going deep. You aren't afraid to dive into Tensor
  
  RT‑LLM source code, write custom C++ / Python bindings, or debug NVLink topology issues.

• You know when to use an off‑the‑shelf solution and when we need to build a custom solution because the upstream tools (like standard Kubernetes networking) are too slow for our needs.

• Deep knowledge of NCCL, NVSHMEM, and UCX.

• Experience with GPUDirect Storage (GDS) or high‑performance file systems like Weka or 3FS.

• Familiarity with Tensor
  
  RT‑LLM, vLLM, or Sglang.

• Experience running low‑level benchmarks to "qualify" new hardware clusters.

• Ble…