Senior Software Engineer, Machine Learning Inference

Posted 4 Days Ago
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Santa Clara, CA, USA
In-Office
152K-288K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
As a Senior Software Engineer at NVIDIA, you will design and implement inference software optimizations for AI applications using TensorRT, collaborating with deep learning experts to influence hardware and software design.
Summary Generated by Built In

At NVIDIA, we're at the forefront of innovation, driving advancements in AI and machine learning to solve some of the world’s most challenging problems. We're seeking talented and motivated engineers to join our TensorRT team in developing the industry-leading deep learning inference software for NVIDIA AI accelerators. 

As a Senior Software Engineer in the TensorRT team, you will be responsible for designing and implementing inference software optimizations to power AI applications on NVIDIA GPUs. If you're ready to take on challenging projects and make a significant impact in a company that values creativity, excellence, and collaboration, we want to hear from you!

What you’ll be doing:

  • Design, develop and optimize NVIDIA TensorRT and TensorRT-LLM to supercharge inference applications for datacenter, workstations, and PCs.

  • Develop software in C++, Python, and CUDA for seamless and efficient deployment of state-of-the-art LLMs and Generative AI models.

  • Collaborate with deep learning experts and GPU architects throughout the company to influence Hardware and Software design for inference.

What we need to see:

  • BS, MS, PhD or equivalent experience in Computer Science, Computer Engineering or a related field.

  • 4+ years of software development experience on a large codebase or project.

  • Strong proficiency in C++ (required), Rust or Python programming languages.

  • Experience in developing Deep Learning Frameworks, Compilers, or System Software.

  • Excellent problem-solving skills and passion to learn and work effectively in a fast-paced, collaborative environment.

  • Strong communication skills and the ability to articulate complex technical concepts.

Ways to stand out from the crowd:

  • Experience in developing inference backends and compilers for GPUs.

  • Knowledge of Machine Learning techniques and GPU programming with CUDA or OpenCL.

  • Background in working with LLM inference frameworks like TensorRT-LLM, vLLM, SGLang.

  • Experience working with deep learning frameworks like TensorRT, PyTorch, JAX.

  • Knowledge of close-to-metal performance analysis, optimization techniques, and tools.

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 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 April 14, 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.

Top Skills

C++
Cuda
Python
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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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