AIInferencePerformanceEngineer-NewCollegeGrad2026

We optimize and benchmark GenAI inference on NVIDIA's latest accelerators, defining the industry’s performance standards across language models, video generation, and speech workloads. We work directly within TensorRT-LLM, SGLang, and vLLM, building the tools that evaluate serving performance at scale. This team sits at the intersection of GPU performance engineering and public accountability. **What You Will Be Doing:** * Drive industry benchmark results: own the end-to-end optimization pipeline, implement and integrate optimizations in quantization, scheduling, memory management, and distributed inference across TensorRT-LLM, SGLang, and vLLM. * Define and optimize cutting-edge workloads: identify and shape next-generation inference benchmarks, multi-turn coding, agentic workflows, and other emerging AI use cases. Collaborate with framework and kernel teams to push performance to its extreme on large-scale LLM-MoE models, vision-language models, video diffusion models, recommendation, and speech workloads. * Architect distributed inference: Design and optimize execution from single-GPU to rack-scale clusters, managing performance across clusters of GPUs. * Establish performance methodology: Apply roofline analysis and systematic profiling to decompose bottlenecks across CUDA kernels, frameworks, and serving layers. * Influence the ecosystem: contribute to TensorRT-LLM, vLLM, SGLang, and other open-source projects. Partner with architecture, kernel, and compiler teams to shape GPU roadmaps based on real workload data. * Technical Leadership: Raise the technical bar for the team, drive cross-functional execution on tight benchmark timelines, and lead a world-class team. **What We Need To See:** * BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience. * 2+ years of relevant software development experience. * Strong Python or C++ programming, software design, and software engineering skills. * Expertise with a DL fr