NVIDIADeep Learning Software Engineering Intern, Test Development - 2027
NVIDIA is hiring a Deep Learning Software Engineering Intern, Test Development - 2027 in Shanghai, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, TensorFlow and Edge LLM.
- Level classifies it as an internship.
- Level lists 2,700+ other open roles at NVIDIA.
AI in this role
Perform GPU software testing and test automation improvement for deep learning products as a software engineering intern.
We are now looking for a Deep Learning Software Test Development Engineering Intern.
The position is in NVIDIA Deep Learning Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance of NVIDIA‘s Deep Learning software and GPU Infrastructure for autonomous driving, healthcare, speech recognition, natural language processing, and a wide variety of other AI scenarios. This team collaborates with multiple AI product teams to develop new products; derive and improve complex test plans; and improve our workflow processes for a diverse range of GPU computing platforms. You should grow with being in the critical path supporting developers working for billion-dollar business lines as well as intimately understanding the values of responsiveness, thoroughness and teamwork. You should constantly foster and implement efficiency improvements across your domain. Join the team which is building software which will be used by the entire world!
What you’ll be doing:
GPU Software testing and test automation improvement for NVIDIA Deep Learning Software products, such as Edge-LLM, TensorRT, NVIDIA optimized Frameworks (E.g. TensorFlow, PyTorch, MxNET, etc.)
Be responsible for functionality, compatibility, and performance tests in DL SW stack release.
Work with development teams to triage issues, root cause analysis, verify fixes, define new tests, improve test plans.
Utilize AI-powered tools to improve efficiency and quality, including test case/plan/script generation, defect detection, CBTP, bug fixing and day to day assistance.
What we need to see:
Pursuing MS or higher degree in CS/EE/CE.
Scripting language (Python, Perl, bash), Linux knowledge is required.
Experiences in C/C++ programming is a plus.
Familiarity any Deep Learning Framework is a strong plus.
Good communication skills, fluent oral and written English.
Experience with AI tools.
Ways to stand out from the crowd:
Familiarity working with NVIDIA GPU hardware is a strong plus.
Background with NVIDIA GPU Computing (CUDA) is a strong plus.
Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows or enhance process automation.
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) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
How we rate this
Deep Learning Software Engineering Intern, Test Development - 2027 at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ 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
Questions you could be asked
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- What have you built with speech recognition or text-to-speech, and where did it break?
- Tell me about a project where software testing was part of your work. What did you do?
- Tell me about a project where test automation was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: NLP, Speech, Software Testing, Test Automation, and Deep learning. 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.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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