Senior Software Engineer, Deep Learning Inference - TensorRT

NVIDIA•Santa Clara, CA

1d•Hybrid

About The Position

We are now looking for a Senior Software Engineer for Deep Learning Inference! Would you like to make a big impact in Deep Learning by helping build a state-of-the-art inference framework for accelerating Deep Learning models, especially Large Language Models, on NVIDIA GPUs? We are now welcoming exceptional software engineers to apply to Senior Engineering positions in the Deep Learning Inference TensorRT software team. What you’ll be doing: Craft and develop robust inferencing software that can be scaled to multiple platforms for functionality and performance Develop components of TensorRT, NVIDIA’s SDK for high-performance deep learning inference. Closely follow academic developments in the field of artificial intelligence and feature update TensorRT Use C++ and Python to build graph parsers, optimizers, and tools for effective deployment of trained deep learning models. Collaborate with teams of deep learning experts, GPU architects and DevOps engineers across diverse teams. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you. Come, join our TensorRT Workflows team and help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field. #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 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. Applications for this job will be accepted at least until February 13, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse 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. NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Requirements

A Bachelor's, Master's, PhD or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering or related field.
3+ years of software development experience.
Strong experience with the latest C++ standardsC++11/C++14/C++17/C++20, etc..
Strong grasp of Machine Learning concepts.
Experience and knowledge in Computer Architecture, Data Structures, Algorithms.
Excellent communication skills, and an aptitude for collaboration and teamwork.

Nice To Haves

Experience developing System Software.
Proficiency in Python as well as Background in GPU kernel programming using CUDA or OpenCL.
Experience in software performance benchmarking, profiling, and optimizations.
Background in compiler development
Experience in working with TensorRT, PyTorch, TensorFlow, ONNX Runtime or other ML frameworks.

Responsibilities

Craft and develop robust inferencing software that can be scaled to multiple platforms for functionality and performance
Develop components of TensorRT, NVIDIA’s SDK for high-performance deep learning inference.
Closely follow academic developments in the field of artificial intelligence and feature update TensorRT
Use C++ and Python to build graph parsers, optimizers, and tools for effective deployment of trained deep learning models.
Collaborate with teams of deep learning experts, GPU architects and DevOps engineers across diverse teams.