AI Computing Software Development Engineer, TensorRT-LLM

Posted Yesterday
Be an Early Applicant
2 Locations
In-Office
Mid level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and optimize scalable LLM inference software and kernels, profile and improve performance, implement runtime features for new LLM models, and collaborate across research, software, and product teams.
Summary Generated by Built In

We are now looking for a Software Development Engineer for LLM inference!

NVIDIA is hiring software engineers for its TensorRT-LLM team. Academic and commercial groups around the world are using GPUs to power a revolution in deep learning-powered AI, enabling breakthroughs in areas like LLM, Generative AI, and Agentic AI. Become part of the group building the inference software which will be used across our product lines! The ability to work on a fast-paced delivery-focused team is required and excellent interpersonal skills are a must.

What you will be doing:

  • Craft and develop robust inference software that can be scaled to multiple platforms for functionality and performance

  • Performance analysis and optimization for Large Language Model (LLM) inference

  • Closely follow academic and industrial developments in the field of artificial intelligence and feature update TensorRT-LLM

  • Implement kernels and runtime features to support new LLM models and inference algorithms

  • Provide feedback into the architecture and hardware design and development

  • Collaborate across the company to guide the direction of deep learning inference, working with software, research and product teams

What we need to see:

  • Master or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree (or equivalent experience)

  • 3+ years of relevant software development experience.

  • Excellent Python programming skills, software design, and software engineering skills

  • Awareness of the latest developments in LLM architectures and LLM inference techniques

  • Experience working with deep learning frameworks like PyTorch and HuggingFace

  • Proactive and able to work without supervision

  • Excellent written and oral communication skills in English

Ways to stand out from the crowd:

  • Prior experience with a LLM inference framework (TensorRT-LLM, SGLang, vLLM, lamma.cpp, MLC-LLM, etc.) or a DL compiler in inference, deployment, algorithms, or implementation

  • Good understanding about the underlying operations inside the LLM inference frameworks or LLM inference end-to-end workflow

  • Experience in performance modeling, profiling, debugging, and code optimization of a DL/HPC/high-performance application

  • Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.

  • Architectural knowledge of CPU and GPU

  • GPU programming experience (CUDA or OpenCL)

NVIDIA is widely considered to be one of technology’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. Does the idea of contributing to and pushing the boundaries of state-of-the-art AI and Compute systems excite you? Interested in getting exposure to the entire DL SW stack? Come join us and help build the GPU-accelerated DL platform used worldwide.

With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the most desirable employers in the world. We have some of the most brilliant and talented people in the world working for us. If you are creative, autonomous and love a challenge, we want to hear from you. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Skills Required

  • Master's degree or higher in Computer Engineering, Computer Science, Applied Mathematics, or related field (or equivalent experience)
  • 3+ years of relevant software development experience
  • Excellent Python programming skills and strong software design and engineering abilities
  • Awareness of latest LLM architectures and LLM inference techniques
  • Experience working with deep learning frameworks such as PyTorch and HuggingFace
  • Excellent written and oral communication skills in English
  • Proactive and able to work without supervision
  • Prior experience with LLM inference frameworks (TensorRT-LLM, SGLang, vLLM, llama.cpp, MLC-LLM) or DL compilers
  • Excellent C/C++ programming and software design skills, including debugging and performance analysis
  • GPU programming experience (CUDA or OpenCL) and architectural knowledge of CPU and GPU
  • Experience in performance modeling, profiling, debugging, and optimization of DL/HPC/high-performance applications

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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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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