深圳招聘

We are looking for a Senior Software QA engineer in NVIDIA’s Deep Learning SWQA team.

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:

As a Software QA Engineer who is engaged in the success of this team and company, you will devote time to process definition and improvement, come up with creative solutions to challenging problems to help us achieve our goals more efficiently.

GPU testing for Deep Learning products to ensure functionality, compatibility and performance for various SDK (cuDNN, TensorRT, etc) and Frameworks.

Be responsible for Deep Learning Software test development and enhancement (Driver, SDK, and Frameworks).

Work with development teams to triage issues, do root cause analysis, verify fixes, define new tests, improve test plans.

What we need to see:

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

5+ years of Software Quality Assurance or Test automation background with knowledge of test infrastructure and strong analysis skills.

Scripting language (Python, Perl, bash) knowledge and UNIX/Linux experience.

Good C/C++ development experience.

Excellent English written and oral communication skills.

Able to juggle conflicting/changing priorities and maintain a positive attitude while experiencing challenging and dynamic schedules.

Ways to stand out from the crowd:

Familiarity with NVIDIA GPU hardware.

Understanding and working knowledge with any Deep Learning Framework.

Working knowledge of NVIDIA GPU Computing (CUDA) and CUDA libraries for Deep Learning.

Experience in VectorCAST, Bullseye, Gcov, or Coverity tools.

Automation experience.

关于 NVIDIA-人工智能计算领域的领导者
从游戏到人工智能计算, 实践证明,GPU 能够非常有效地解决计算机科学领域中一些极其复杂的问题。GPU 一开始用作模拟人类想象力的引擎，创造出视频游戏和好莱坞电影中令人惊叹的虚拟世界。如今，NVIDIA GPU 用于模拟人类智力，运行深度学习算法，并在能够感知和理解世界的计算机、机器人和自动驾驶汽车中发挥大脑的作用。NVIDIA 的“AI 计算公司”名头越来越为人所知。
这正是我们毕生的工作 - 增强人类的想象力和智力。