Seeking a skilled Senior Software Tools Development Engineer to enhance our GPU SWQA team. This role requires significant expertise in AI for test automation, deep Linux OS knowledge, and a strong grasp of system and PC I/O architecture, including common bus interfaces like PCI-E, USB, and SATA. Familiarity with PC architecture components is a plus.

Develop and deploy automated tests leveraging AI for NVIDIA's driver software and SDKs on Linux. Build essential tools, utilities, and frameworks using Python to streamline GPU testing workflows. Automate and optimize testing processes, analyze results, and pinpoint defects. Drive test automation initiatives and innovate testing strategies. Isolate, debug, and resolve complex technical issues, contributing to solutions and tracking them to closure. Measure and enhance code coverage for software under test. Create applications and tools that accelerate development and testing, writing efficient, maintainable, and well-documented code. Validate compatibility across products and interfaces, ensuring robust software performance through comprehensive testing.

Conduct peer code reviews, offering insights on performance, scalability, and accuracy. Effectively estimate and prioritize tasks to ensure timely delivery. This role offers the chance to tackle challenging technical and process problems, collaborating with leadership and providing clear progress reports.

A Bachelor's or Master's degree in Computer Science, Electronics & Telecommunication, or equivalent experience is required, along with a strong academic record. Minimum of 4 years of programming experience in Python or a similar language, demonstrating proficiency in Object-Oriented Programming concepts. Practical experience developing Python scripts with application development fundamentals such as dictionaries, tuples, RegEx, and PIP is essential. Working knowledge of databases and storage technologies including SQL, MongoDB, and Elasticsearch is necessary.

Solid understanding of OS fundamentals and PC hardware tuning, particularly in Linux, Unix, and Windows environments. Prior experience utilizing AI tools for test case automation and end-to-end QA workflows is critical. Excellent interpersonal and cross-team communication skills are expected, alongside strong analytical and problem-solving abilities. Candidates should be highly organized, proactive, and possess a keen ability to learn. The capacity to collaborate effectively within a fast-paced engineering team is crucial.