Senior Software Test Development Engineer - Deep Learning

Overview

We are looking for a Software Test development engineer in NVIDIA’s Deep Learning SWQA team. The position is in NVIDIA Deep Learning and AI 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. We collaborate 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 collaboration. You should constantly champion 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 :

Work closely with global multi-functional teams to understand the test requirements and take ownership of product quality.

Plan / design / implement / report / automate test plan / test case / test reports.

Run bug lifecycle and co-work with inter-groups to work towards solutions.

Automate test cases and assist in the architecture, crafting and implementing of test frameworks.

In-house repro and verify customer issues / fixes.

What we need to see :

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

4+ 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++ software development or test development experience.

Good user / development experiences of virtualization like VM & Docker container.

Understanding and working knowledge with any Deep Learning Framework and models especially in end-to-end customer scenarios.

Experience in validating Deep Learning software and Deep Learning models.

Experience in using AI development tools for test plans creation, test cases development and test cases automation.

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

Excellent English written and oral communication skills.

Ways to stand out from the crowd :

Familiarity with NVIDIA GPU hardware products (Tesla, Tegra, DGX, etc.); working knowledge of NVIDIA GPU Computing (CUDA) and CUDA libraries for Deep Learning.

Background in building models and AI-based infrastructure to improve test automation.

Experience with LLM inference frameworks (TRT-LLM, vLLM, SGLang, etc.) andfamiliar with running various AI workloads

Background in validating Data Center GPU based infrastructure (multi-GPUS, multi-nodes, cluster).

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

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 168,000 USD - 264,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until October 14, 2025. 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) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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