- More than 6 years of experience in full-stack LLM/VLM inference performance involving models, serving, distributed runtimes, kernels, and hardware. Your efforts result 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. 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.
Ways to stand out from the crowd
- Contributions to one or more high-performance AI projects. Examples include 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. You validate generated changes through reproducible, human-reviewed tests for performance, model quality, and correctness.
- 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.Successfully carrying these out will shape the future of AI inference performance!
With competitive salaries and a generous benefits package (www.nvidiabenefits.com ), we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.