Hippocratic AI builds the safest generative AI healthcare agent for health systems, payors, and pharma. Over 180 million clinical interactions across 1,000+ use cases with 60+ partners worldwide. Backed by General Catalyst, Kleiner Perkins and a16z.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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, InfiniBand, 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
More about this role
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, InfiniBand, 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...
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