Deep Learning Software QA Engineer Intern

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow, Deep Learning, Natural Language Processing (NLP)

Job Description

We are now looking for a Deep Learning Software QA Engineer 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 cuDNN, 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. 

What we need to see:

• BS 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 

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