stninc - Inference & Workload Optimization Lead (Horizon AI)
Requirements
• Deep GPU performance engineering: profiling, memory and bandwidth behaviour, and enough kernel-level understanding to know where time actually goes - Serving stack depth, for example vLLM, SGLang, or TensorRT-LLM, including batching, KV cache management, and quantization trade-offs - Distributed training and inference experience at scale: tensor, pipeline, and data parallelism, and collective communication tuning such as NCCL • Fluency with profiling tooling, for example Nsight, DCGM, and framework-level profilers • The ability to explain a performance finding to a customer and to encode it for an agent. These are different skills and this role needs both • Nice to have: CUDA kernel work, open-weight model deployment at scale, benchmark design, prior customer-facing technical experience
Responsibilities
• Profile customer training and inference workloads and find the real bottlenecks: data loading, collective communication, memory bandwidth, kernel efficiency, batching, parallelism layout • Establish performance baselines and a definition of good per hardware generation, starting with current-generation NVIDIA hardware • Turn that expertise into agent-executable optimization, so a fix reaches every cluster rather than the one you happened to look at • Work with committed-capacity customers, directly or through the operating teams, on workload tuning • Own internal model serving as a secondary responsibility, including cost and latency-aware routing and model version migration • Advise on GPU allocation between internal inference and customer workloads
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