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NVIDIA Off Campus Drive 2026 – Test & Tools Development Engineer
NVIDIA
9 May 2026 - 22 Jun 2026
Drive Ended
Description
NVIDIA Off Campus Drive 2026 – Details:
| Job Role | Test & Tools Development Engineer |
|---|---|
| Job Category | Off-Campus Drive |
| Qualification | B.E/B.Tech/B.Sc |
| Batch | 2026 |
| Experience | Freshers |
| Job Location | Pune |
| Last Date | ASAP |
Detailed Eligibility:
Test and Tools Development Engineer – New College Grad 2026:
What we need to see:
- Pursuing or recently completed a Bachelor’s Degree in Computer Science or equivalent
- Strong Python fundamentals — able to write clean, testable code and reason about structure beyond single scripts
- Hands-on exposure to AI-native development workflows — Claude Code, Cursor, Codex, or prompt engineering through coursework, internships, hackathons, or personal projects
- At least one project, open-source contribution, or coursework example where you coordinated an LLM into a working system end-to-end
- Foundational understanding of software testing, CI/CD concepts, or quality engineering principles
- Awareness of common LLM failure modes — hallucination, context limits, tool misuse — and curiosity about how to mitigate them
What you’ll be doing:
- Build multi-agent pipelines for automated test generation, log analysis, failure triage, and bug-filing workflows, working alongside senior engineers on well-scoped pieces of the system
- Contribute to evaluation systems that measure agent output quality — writing test cases, analyzing failure patterns, and extending eval frameworks under senior mentorship
- Add instrumentation, logging, and monitoring to agentic workflows so failures are visible and debuggable — learning the systems-thinking that makes infrastructure trustworthy
- Grow your judgment on where LLMs help and where they fail. Learn how to build solutions around both with mentorship.
Eligibility Criteria
- Pursuing or recently completed a Bachelor’s Degree in Computer Science or equivalent
- Strong Python fundamentals — able to write clean, testable code and reason about structure beyond single scripts
- Hands-on exposure to AI-native development workflows — Claude Code, Cursor, Codex, or prompt engineering through coursework, internships, hackathons, or personal projects
- At least one project, open-source contribution, or coursework example where you coordinated an LLM into a working system end-to-end
- Foundational understanding of software testing, CI/CD concepts, or quality engineering principles
- Awareness of common LLM failure modes — hallucination, context limits, tool misuse — and curiosity about how to mitigate them
