Deep Learning Software Engineer, TensorRT Performance

NVIDIA

. Establish performance benchmarking methodologies and analysis workflows for NVIDIA's inference ecosystem .

Posted 10/12/2026full-timeRemote • California • United StatesJuniorMid-Level💰 $124,000 - $195,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in performance benchmarking methodologies and optimization within NVIDIA's inference ecosystem, with strong programming skills in C++ and Python. Proficient in deep learning frameworks and libraries, with a focus on performance analysis and architectural knowledge of GPUs.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingPerformance AnalysisDeep Learning FrameworksGPU Architecture Knowledge

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Software EngineeringQuantizationMemory ManagementDistributed InferencePerformance OptimizationCUDATensorRTPyTorchTensorFlowJAX

Tools & Technologies

TensorRT-EdgeLLMTorch-TensorRTONNXVLLMSGLangFlashInferTileIRCuTeDSLCutlassTriton

Industry Keywords

Deep LearningInference FrameworksGenerative AIAutomotiveRoboticsImage UnderstandingSpeech UnderstandingLow-Latency SystemsResource-Constrained SystemsEmbedded AI Pipelines

Tech Stack

Tools & technologies

PythonPyTorchTensorflowC++

About the role

Key responsibilities & impact

Establish performance benchmarking methodologies and analysis workflows for NVIDIA's inference ecosystem
Identify performance issues and optimization opportunities across TensorRT, TensorRT-EdgeLLM, and Torch-TensorRT
Contribute features and code to NVIDIA and open-source inference frameworks
Develop optimized model pipelines involving quantization, scheduling, memory management, and distributed inference
Collaborate with teams across generative AI, automotive, robotics, image understanding, and speech understanding
Scale deep learning model performance across architectures and NVIDIA accelerators

Requirements

What you’ll need

Bachelor's, Master's, PhD, or equivalent experience in relevant fields (Computer Science, Computer Engineering, EECS, AI)
2 years of relevant software development experience
Strong C++ programming skills
Strong Python programming skills
Strong software engineering skills
Experience with DL frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX) and inference libraries (e.g. TensorRT, TensorRT-LLM, vLLM, SGLang, FlashInfer)
Experience with performance analysis and performance optimization
Strong foundation and architectural knowledge of GPUs (standout)
Deep understanding of modern deep learning models and workloads (standout)
Proficiency in a deep learning programming domain specific language such as CUDA, TileIR, CuTeDSL, cutlass, or Triton (standout)
Prior contributions to major LLM inference frameworks or experience with graph compilers in deep learning inference (standout)
Prior experience optimizing performance for low-latency, resource-constrained systems or embedded AI pipelines (standout)

Benefits

Comp & perks

Equity
Benefits 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score