Senior Deep Learning Software Engineer, TensorRT Performance

Details

We are seeking an experienced Deep Learning Engineer to analyze and improve the performance of NVIDIA's inference ecosystem. The team develops GPU-accelerated deep learning inference software such as TensorRT, DL benchmarking software, and performant solutions for deployment and serving of models across datacenter GPUs and edge SoCs.

Responsibilities

• Establish performance benchmarking methodologies and analysis workflows; identify performance issues and opportunities across NVIDIA's inference ecosystem (e.g., TensorRT, TensorRT-EdgeLLM, Torch-TensorRT).
• Contribute features and code to NVIDIA and open-source inference frameworks (including but not limited to TensorRT, TensorRT-EdgeLLM, Torch-TensorRT).
• Develop model pipelines optimized for inference performance, including work in quantization, scheduling, memory management, and distributed inference.
• Implement graph compiler algorithms, frontend operators, and code generators across the inference stack.
• Perform performance modeling, analysis, and kernel development to scale model performance across different NVIDIA accelerator architectures.
• Collaborate with cross-functional teams across generative AI, automotive, robotics, image understanding, and speech domains.

Requirements

• Bachelors, Masters, PhD, or equivalent experience in Computer Science, Computer Engineering, EECS, AI or related fields.
• At least 3 years of relevant software development experience.
• Strong C++ and Python programming and software engineering skills.
• Experience with deep learning frameworks (examples given: PyTorch, JAX, TensorFlow, ONNX).
• Experience with inference libraries (examples given: TensorRT, TensorRT-LLM, vLLM, SGLang, FlashInfer).
• Experience with performance analysis and performance optimization.

Ways to stand out

• Strong foundation and architectural knowledge of GPUs.
• Deep understanding of modern deep learning models and workloads (e.g., Transformers, Recommenders, ASR, TTS, Visual Understanding).
• Proficiency in deep learning programming DSLs and GPU programming (examples given: CUDA, TileIR, CuTeDSL, cutlass, Triton).
• Prior contributions to major LLM inference frameworks (e.g., vLLM) or experience with graph compilers for deep learning inference (e.g., TorchDynamo, TorchInductor).
• Prior experience optimizing for low-latency, resource-constrained systems or embedded AI pipelines (e.g., Jetson or other edge AI accelerators).

Compensation & Benefits

• Base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.
• You will also be eligible for equity and benefits.

Other information

• Location: Santa Clara, CA, United States.
• Time type: Full time. Posted as #LI-Hybrid.
• Applications accepted at least until March 26, 2026.
• NVIDIA uses AI tools in its recruiting processes. NVIDIA is an equal opportunity employer.