Level

NVIDIA

GPU Computing Software Engineering Intern, Autonomous Driving - 2027

NVIDIA is hiring a GPU Computing Software Engineering Intern, Autonomous Driving - 2027 in Shanghai, China. Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Level rates this role Uses AI ●●○○: an ordinary role that requires AI tools.
  • Tools and skills the posting cites include PyTorch.
  • Level classifies it as an internship.
  • Level lists 2,700+ other open roles at NVIDIA.
  • The role was posted in the last few days.

AI in this role

pytorch

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. This is our life’s work — to amplify human imagination and intelligence. AI becomes more and more important in self-driving car. NVIDIA is at the forefront of the AI-City and self-driving revolution and providing powerful solutions for them. All these solutions are based on GPU-accelerated libraries, such as CUDA, cuDNN and TensorRT, etc.

Now, we are now looking for CPU computing interns based in Shanghai. Join the team to provide the powerful AI solution to the entire world!

What you’ll be doing:

  • Analyze the GPU computing issues and write some test code for them.

  • Write some documentation about the analysis of the issue.

What we need to see:

  • Pursuing BS or higher degree in CS/EE/CE or equivalent.

  • Good programming experience on Linux OS.

  • Good programming skills in C and C++, python is a plus.

  • Strong knowledge about Linux Shell and Makefile.

  • Have knowledge about the popular inference network and Deep Learning layers.

  • Experience with deep learning Pytorch framework.

  • Good at read and written in English.

Ways to stand out from the crowd:

  • Familiarity with NVIDIA GPU hardware products (Tesla, Jetson, DGX, etc).

  • Working knowledge of NVIDIA GPU Computing (CUDA) and CUDA-X libraries for Deep Learning, especially CUDA.

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, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

How we rate this

GPU Computing Software Engineering Intern, Autonomous Driving - 2027 at NVIDIA rates 43 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.

Classification

Uses AI. An ordinary role that requires AI tools.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

PyTorch

Questions you could be asked

  1. What's a project where you used PyTorch hands-on?
  2. This role expects you to use AI tools as part of the job. Which ones have you used, and for what?
  3. Tell me about a time an AI tool got something wrong. How did you catch it?

Adapt your resume

  • List these exact terms on your resume: PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.

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