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Senior Software Engineer - AI Inference Performance

NVIDIA· Engineer, Sys SW
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Santa Clara, CA$184k - $287.5k + equityFull-timePosted 1mo ago

About the role

Senior Software Engineer focusing on AI inference performance for LLM/VLM workloads on NVIDIA GPUs. Hands-on role turning performance models and profiler data into optimized code, collaborating across model, framework, kernel, networking, and GPU architecture teams to improve latency, throughput, efficiency, and scale.

Responsibilities

  • Lead end-to-end analysis of LLM/VLM inference processes. Define representative prefill and decode workloads. Optimize time to first token, inter-token latency, P99 end-to-end latency, processing efficiency, and key-value (KV) cache capacity. For multimodal models, isolate preprocessing, encoder, and decoder costs.
  • Build speed-of-light and roofline models to quantify performance headroom. Connect 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. 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. Choose them 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. Use CUDA, CUTLASS, Triton, or related technologies.
  • Establish repeatable benchmarks, canonical run records, and performance regression gates. Manage aspects such as model, precision, hardware, topology, software, features, and workload; Balance between performance and accuracy. Collaborate across with various teams and contribute high-quality upgrades to TensorRT-LLM, vLLM, SGLang, or associated projects.

Qualifications

  • More than 6 years of experience in full-stack LLM/VLM inference performance involving models, serving, distributed runtimes, kernels, and hardware.
  • 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. You convert profiles into testable hypotheses and validated progress.
  • 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. You can choose techniques for the workload, including batching, scheduling, KV-cache management, quantization, speculative decoding, and various parallelism strategies
  • Understanding of distributed systems and networking for accelerated computing. You can reason 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.

Skills

Required:PythonC++CUDA
Nice to have:Rust

Benefits

  • Competitive salaries
  • Generous benefits package
  • Equity eligibility
  • Access to NVIDIA benefits portal
Last updated Oct 2, 2026. Interested? Apply on the company site.

About NVIDIA

World Leader in AI Computing

NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.

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