Software Qa Test Developer

NVIDIA NVIDIA · Semiconductors · Bangalore, India

NVIDIA is seeking a Software QA Test and Tool Developer for their Automotive Platform team. The role involves designing, executing, and automating test cases for automotive platforms, architecting test automation frameworks, and developing test libraries. A key aspect is understanding and evaluating AI-native development tools, LLM failure modes, and building evaluation frameworks for AI-generated outputs, with experience in prompt engineering and LLM-based agents being a plus.

What you'd actually do

  1. Design, execute, and automate comprehensive test cases and test scenarios to validate our automotive platforms using various test methodologies to identify and track actionable defects and track them to closure.
  2. Participate in deep-dive reviews of product requirements and technical designs, providing critical feedback to ensure features are built for testability and security from day one.
  3. Partner closely with project management, hardware teams, and software developers to provide rigorous technical analysis of bugs and publish data-driven statistical reports for global team members.
  4. Architect and maintain a distributed test automation framework capable of managing high concurrency workloads across an extensive automation farm of hundreds of concurrent systems.
  5. Develop sophisticated test libraries and automation solutions to accelerate development cycles and expand automated test coverage for reliable and 100%

Skills

Required

  • SWQA Test development & Automation engineering
  • System SW validation
  • Python or C++
  • AI-native development tools
  • LLM failure modes
  • evaluation frameworks for AI-generated outputs

Nice to have

  • QNX
  • Linux testing
  • 1G & 10G Ethernet Testing
  • Socket programming
  • C / C++ coding
  • prompt engineering
  • LLM-based agents
  • CI/CD pipeline

What the JD emphasized

  • 5+ years of proven experience in SWQA Test development & Automation engineering.
  • Deep familiarity with AI-native development tools such as Claude Code, Cursor, or LLM APIs to optimize engineering velocity.
  • A clear understanding of LLM failure modes—including hallucination and context degradation—and experience building evaluation frameworks for AI-generated outputs.

Other signals

  • AI-native development tools
  • LLM failure modes
  • evaluation frameworks for AI-generated outputs
  • prompt engineering
  • LLM-based agents