Inference Performance Engineer, Agent Driven Inference Optimization

2100 NVIDIA USA; Santa Clara (CA); Híbrido; USD 124.000 - 242.000; Jornada completa

Descripción de la vacante

NVIDIA Corporation is seeking a Senior Inference Performance Engineer to push the performance envelope of large-scale AI inference on NVIDIA GPUs. You will architect optimized workflows, profile and tune inference stacks, and drive measurable wins across TensorRT-LLM, vLLM, and related frameworks.

You will collaborate with TensorRT-LLM, SGLang, and GPU teams to translate profiling insights into deployable improvements, including patches, kernels, and deployment recipes.

Formación

• 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.
• Extensive knowledge of AI model execution efficiency and optimization including batching, throughput-latency tradeoffs, KV cache and memory limits, parallel processing, MoE serving, quantization, and serving SLAs.
• Hands-on benchmarking and profiling GPU workloads using Nsight Systems, Nsight Compute, CUPTI, or PyTorch profiler; interpreting kernel-level 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 and reproducible benchmarks.
• Strong written and verbal communication skills to explain performance tradeoffs clearly.

Responsabilidades

• Distill performance instincts into reusable skills, workflows, and autonomous agent tasks.
• Review agent experiments, validate findings, and curate best-known configurations.
• Improve AI inference workloads to increase throughput-per-GPU and interactivity via configuration, batching, parallelism, KV cache, quantization, and decoding settings.
• Profile workloads across TensorRT-LLM, SGLang, vLLM, Dynamo on NVIDIA GPUs.
• Use Nsight Systems, kernel traces, and internal tools to measure performance.
• Land upstream improvements to serving frameworks, kernels, and deployment recipes while preserving model accuracy.

Conocimientos

Python

C++/CUDA

Nsight Systems

Nsight Compute

PyTorch profiler

Performance optimization

Educación

BS/MS/PhD in Computer Science/Engineering or related

Herramientas

TensorRT-LLM

SGLang

vLLM

CUDA kernels

NCCL/SHMEM

Descripción del empleo

Overview

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!

Key Responsibilities

• 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.

Required Qualifications

• 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.
• 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.
• 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.

Preferred Experience (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.

Compensation and Benefits

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 other objective job-related factors.

The expected base salary ranges for this position are: 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.

Application & Closing

Applications for this job will be accepted at least until November 13, 2026.

This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

Equal Opportunity & Diversity Statement

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.

About NVIDIA

NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.