DL Inference Performance Engineer (TensorRT)

NVIDIA Corporation; Santa Clara (CA); On-site; USD 124,000 - 242,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation in Santa Clara is seeking a Deep Learning Software Engineer for TensorRT performance to optimize the inference stack. You will analyze and improve performance across TensorRT, Torch-TensorRT, and related NVIDIA OSS projects, shaping low-latency DL workloads.

The role requires strong C++/Python skills and hands-on experience with DL frameworks, quantization and scheduling. Collaboration across AI and automotive domains is expected, with a focus on scalable inference across

Qualifications

• Bachelor's, Master's or PhD or equivalent experience in CS/CE/AI.
• 2 years of relevant software development experience.
• Strong C++, Python programming and software engineering skills.
• Experience with DL frameworks (PyTorch, JAX, TensorFlow, ONNX) and inference libraries (TensorRT, TensorRT-LLM, vLLM, SGLang, FlashInfer).
• Experience with performance analysis and performance optimization.

Responsibilities

• Establish groundbreaking performance benchmarking methodologies and analysis workflows.
• Contribute features and code to NVIDIA/OSS inference frameworks.
• Develop new model pipelines with optimized performance including quantization, scheduling and memory management.
• Collaborate with cross-functional teams across AI, automotive, robotics, and more.
• Scale performance of deep learning models across different architectures and NVIDIA accelerators.

Skills

C++

Python

DL frameworks

Education

Bachelor's/Master's/PhD or equivalent

Tools

TensorRT

TensorRT-LLM

Torch-TensorRT

CUDA

PyTorch

TensorFlow

ONNX

Job description

NVIDIA Corporation in Santa Clara is seeking a Deep Learning Software Engineer for TensorRT performance to optimize the inference stack. You will analyze and improve performance across TensorRT, Torch-TensorRT, and related NVIDIA OSS projects, shaping low-latency DL workloads.

The role requires strong C++/Python skills and hands-on experience with DL frameworks, quantization and scheduling. Collaboration across AI and automotive domains is expected, with a focus on scalable inference across