Job description

NVIDIA is recruiting a Senior Inference Performance Engineer to push NVIDIA's performance limits on large-scale AI inference benchmarks. This position provides an outstanding opportunity to employ your optimization knowledge in an autonomous optimization framework. AI agents use this framework to repeatedly run benchmark, profile, and tune processes, amplifying the impact of every technique you design. If you enjoy extracting maximum performance from GPUs and scaling your skills beyond your individual efforts, this role is a great fit! What you'll be doing: • Distill your performance instincts into reusable skills, workflows, and evidence-backed methodologies that AI agents can complete autonomously. Review agent-generated experiments, validate findings, and curate best-known configurations. • Performance improvement of AI inference workloads that methodically increase throughput-per-GPU and user interactivity by exploring configuration options, parallelism techniques, batching, KV cache handling, quantization, and speculative decoding settings. • Measure and optimize both aggregated and disaggregated serving architectures across TensorRT-LLM, SGLang, vLLM, and Dynamo on NVIDIA's latest GPU platforms. • Profile workloads using Nsight Systems, kernel traces, and internal analysis tools. Use roofline and speed-of-light analysis to find credible headroom and drive fixes from hypothesis to measured wins. • Land improvements upstream: serving framework patches, optimized kernels, and deployment recipes that advance the public Pareto frontier while maintaining strict model correctness. • Collaborate with TensorRT-LLM, SGLang, vLLM, kernel, benchmarking, and GPU architecture teams to convert profiling insights into delivered performance improvements. What we need to see: • BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related field, or equivalent experience. • 3+ years of relevant engineering experience. • Must have: Extensive knowledge of the efficiency and optimization involved in AI model execution, covering continuous batching, throughput-latency tradeoffs, KV cache and memory limitations, parallel processing techniques, MoE serving, quantization, and meeting serving SLAs. • Must have: Hands-on experience benchmarking and profiling GPU workloads using tools such as Nsight Systems, Nsight Compute, CUPTI, or PyTorch profiler, and interpreting kernel-level performance data. • Strong Python engineering skills and the ability to navigate and modify large C++/CUDA serving codebases. • Rigorous experimental methodology with controlled single-variable comparisons, reproducible benchmarks, and evidence-backed optimization decisions. • Strong written and verbal communication skills to explain performance tradeoffs clearly to both humans and documentation for autonomous systems. Ways to stand out from the crowd: • Direct contributions to TensorRT-LLM, vLLM, SGLang, FlashInfer, Dynamo, or comparable inference frameworks. • Experience with disaggregated serving, wide expert-parallel MoE inference, KV cache transfer, or NCCL/NIXL/NVSHMEM communication at multi-node scale. • CUDA kernel authorship or optimization experience on Hopper/Blackwell architectures, focusing on Tensor Cores, TMA, and warp specialization. • Proven results on public inference benchmarks such as MLPerf Inference or SemiAnalysis InferenceX. • Experience building or operating agentic AI workflows to automate engineering tasks. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 11, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Measure and optimize both aggregated and disaggregated serving architectures across TensorRT-LLM, SGLang, vLLM, and Dynamo on NVIDIA's latest GPU platforms.
Profile workloads using Nsight Systems, kernel traces, and internal analysis tools. Use roofline and speed-of-light analysis to find credible headroom and drive fixes from hypothesis to measured wins.
Land improvements upstream: serving framework patches, optimized kernels, and deployment recipes that advance the public Pareto frontier while maintaining strict model correctness.
Collaborate with TensorRT-LLM, SGLang, vLLM, kernel, benchmarking, and GPU architecture teams to convert profiling insights into delivered performance improvements.
BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related field, or equivalent experience.
3+ years of relevant engineering experience.
Must have: Hands-on experience benchmarking and profiling GPU workloads using tools such as Nsight Systems, Nsight Compute, CUPTI, or PyTorch profiler, and interpreting kernel-level performance data.
Strong Python engineering skills and the ability to navigate and modify large C++/CUDA serving codebases.
Rigorous experimental methodology with controlled single-variable comparisons, reproducible benchmarks, and evidence-backed optimization decisions.
Strong written and verbal communication skills to explain performance tradeoffs clearly to both humans and documentation for autonomous systems.
Direct contributions to TensorRT-LLM, vLLM, SGLang, FlashInfer, Dynamo, or comparable inference frameworks.
Experience with disaggregated serving, wide expert-parallel MoE inference, KV cache transfer, or NCCL/NIXL/NVSHMEM communication at multi-node scale.
CUDA kernel authorship or optimization experience on Hopper/Blackwell architectures, focusing on Tensor Cores, TMA, and warp specialization.
Proven results on public inference benchmarks such as MLPerf Inference or SemiAnalysis InferenceX.
Experience building or operating agentic AI workflows to automate engineering tasks.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 11, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

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Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpython

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