LLM Inference Systems Engineer

We’re seeking an inference systems engineer to build the distributed serving and high-performance networking layer behind our large language models. You will own the path from model server to GPU fabric: prefill/decode disaggregation, KV-cache transfer, multi-node execution, and the observability and benchmarks needed to make those systems reliable in production. This role sits in the Inference team within the company’s Model team and partners closely with Model, Infrastructure, and Application engineering.

• Design, implement, and operate disaggregated LLM serving architectures, including independent prefill and decode pools, KV-cache transfer, routing, batching, and failure recovery.

• Optimize multi-node inference across modern GPU systems using NVLink, NVSwitch, NVLS, Infini Band, RoCEv2, GPUDirect RDMA, NCCL, UCX, and related communication paths.

• Build and tune serving systems using SGLang, vLLM, NVIDIA Dynamo, TensorRT-LLM, or comparable frameworks.

• Profile end-to-end performance across compute, memory, network, and storage; improve time to first token, inter-token latency, throughput, tail latency, and cost per token.

• Develop repeatable benchmark and capacity-planning workflows across model architectures, GPU types, parallelism strategies, and concurrency levels.

• Productionize serving on Kubernetes with health checks, autoscaling, safe rollouts, metrics, tracing, and actionable diagnostics.

• Diagnose complex distributed failures such as collective timeouts, topology mismatches, packet loss, congestion, KV-transfer stalls, GPU OOMs, and uneven load.

• Partner with model researchers and platform engineers to launch new models, serving features, and hardware generations safely.

We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Menlo Park office five days a week, unless otherwise specified.

• Production experience building or operating distributed systems, high-performance computing systems, or large-scale ML inference platforms.

• Strong understanding of LLM inference, including tensor/pipeline/data parallelism, continuous batching, KV-cache management, and prefill/decode behavior.

• Hands‑on experience with GPU communication and networking technologies such as NCCL, NVLink/NVSwitch, Infini Band, RoCE, RDMA, UCX, or equivalent systems.

• Strong Python skills and working proficiency in C++ or another systems language.

• Ability to profile and debug performance across application, runtime, kernel, network, and infrastructure layers.

• Experience deploying production workloads on Kubernetes and operating them with clear reliability and observability standards.

• Clear written and verbal communication across research, infrastructure, and product-facing teams.

• Master's or PhD Required

• Direct experience with disaggregated serving, KV-cache transfer, NVIDIA Dynamo/NIXL, SGLang, vLLM, or TensorRT-LLM.

• Experience with NVLS, SHARP, GPUDirect RDMA, UCX, RDMA congestion control, or GPU-cluster topology optimization.

• CUDA, Triton, custom kernel, or low-level GPU performance experience.

• Experience with speculative decoding, multi-LoRA serving, quantization, or cache‑aware routing.

• Contributions to open‑source inference, networking, or distributed‑systems projects.

• Experience benchmarking new GPU platforms and turning results into production architecture decisions.

• Polaris: A Safety-focused LLM Constellation Architecture for Healthcare — https://arxiv.org/abs/

• Polaris 2 —

• Personalized Interactions —

• Human Touch in AI —

• Empathetic Intelligence —

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