About the job
NVIDIA is the platform upon which every new AI-powered application is built. We are seeking a Senior Software Engineer – AI Inference Performance to advance innovative LLM and VLM inference. You will push workloads toward practical performance limits on NVIDIA GPU-accelerated systems. Your work will span models, serving software, distributed runtimes, communication, CUDA kernels, and GPU architecture. Deliver measurable gains in latency, throughput, efficiency, and scale.
This is a hands-on role for an engineer who turns performance models and profiler data into working code. You will collaborate with model, framework, kernel, networking, and GPU architecture teams. You will contribute improvements to open-source inference engines and develop methods that others can reproduce. Your work will improve production deployments and help build future NVIDIA platforms.
Responsibilities
Lead end-to-end analysis of LLM/VLM inference processes, defining representative prefill and decode workloads and optimizing time to first token, inter-token latency, P99 end-to-end latency, processing efficiency, and KV cache capacity.
Build speed-of-light and roofline models to quantify performance headroom, connecting arithmetic intensity, bandwidth, occupancy, memory hierarchy, and communication costs to clear optimization hypotheses.
Profile workloads using NVIDIA Nsight Systems, Nsight Compute, PyTorch Profiler, and custom instrumentation to eliminate bottlenecks in host code, CUDA kernels, memory, communication, and scheduling.
Tune serving hyperparameters and techniques such as batching, KV-cache management, quantization, speculative decoding, CUDA Graphs, and model parallelism based on workload, hardware, model quality, and service-level objectives.
Build and optimize performance-critical kernels including attention, matrix multiplication, mixture-of-experts routing, quantization, and data movement using CUDA, CUTLASS, Triton, or related technologies.
Establish repeatable benchmarks, canonical run records, and performance regression gates while contributing high-quality upgrades to TensorRT-LLM, vLLM, SGLang, or associated projects.
Qualifications
Minimum
More than 6 years of experience in full-stack LLM/VLM inference performance involving models, serving, distributed runtimes, kernels, and hardware resulting in measurable gains in production or production-representative environments.
Strong programming skills in Python, Rust and/or C++, plus hands-on experience with CUDA or another GPU programming environment.
Demonstrated expertise in speed-of-light analysis, roofline models, microbenchmarks, and tools including NVIDIA Nsight Systems and Nsight Compute.
Deep understanding of GPU architecture, including Tensor Cores, memory hierarchy, caches, occupancy, synchronization, and numerical formats across hardware generations.
Practical experience optimizing inference servers and model execution, including batching, scheduling, KV-cache management, quantization, speculative decoding, and various parallelism strategies.
Understanding of distributed systems and networking for accelerated computing, including reasoning about collectives, topology, and scale-up versus scale-out performance.
BS or MS in Computer Science, Computer Engineering, or a related field, or equivalent experience.
Preferred
Contributions to one or more high-performance AI projects such as TensorRT-LLM, vLLM, SGLang, PyTorch, CUDA, Triton, or NCCL.
Experience developing AI-agent-supported performance workflows that automatically gather and analyze profiles, identify bottlenecks, explore serving configurations, or produce optimized runtime and kernel code validated through reproducible tests.
Published research, conference presentations, technical talks, or blog posts that clearly explain inference performance methods and results.
Delivered advancements for new LLM or VLM architectures, long-context inference, mixture-of-experts models, multimodal pipelines, or large-scale distributed serving.