Description
As a Software Engineer Intern at NVIDIA, you will join the PerfLab team and work on cutting-edge projects that involve integrating AI into testing and service workflows. You will collaborate with leading experts to develop tools and technologies that improve troubleshooting, result analysis, and reporting.
Responsibilities:
- Integrate AI into testing and service workflows to improve troubleshooting, result analysis, and reporting
- Develop an MCP tool to analyze service logs
- Create an AI Agent to orchestrate troubleshooting, automate triage, and support root cause analysis across multiple services
- Build AI-assisted analysis of test results to identify performance regressions, anomalies, and trends across game builds, drivers, and hardware configurations
- Connect AI analysis with historical results and known issues to provide context and supporting evidence
- Generate clear, actionable summaries and integrate their delivery into reports, dashboards, and notifications for end-users
- Collaborate with testing engineers and customers to evaluate AI-generated findings and improve their accuracy and usefulness
- Implement observability and feedback loops for safe operation in production
- Propose new architectures and approaches
Requirements:
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Engineering, AI, Data Science, or related field
- Strong programming fundamentals, especially in Python or TypeScript
- Solid understanding of data structures, algorithms, and API design
- Basic knowledge of machine learning concepts, especially retrieval-based or agent-based architectures
- Familiarity with Git, databases, and data pipelines
- Strong problem-solving skills and eagerness to learn new technologies
- Interest in applying AI to performance testing, data analysis, and customer-facing workflows
- Strong collaboration and communication skills, with the ability to learn quickly and work effectively in a dynamic environment
Preferred Qualifications:
- Strong experience with Python and TypeScript
- Exposure to LLM evaluation, structured outputs, or human-in-the-loop review workflows
- Experience with performance testing or test automation, and familiarity with gaming performance metrics and analysis methods
- Experience with RAG pipelines, vector databases, embeddings, or LLM orchestration
- Knowledge of containerization and orchestration