Are you an experienced QA engineer passionate about test automation, AI-driven quality engineering, and system-level software testing? NVIDIA is hiring for a Senior Tools Development Engineer in Pune, offering an opportunity to work on automated testing tools, device drivers, SDKs, and Windows-based systems. This role is particularly relevant for engineers who combine programming skills with debugging, test framework development, and a strong understanding of computer architecture.
🏢 About NVIDIA
NVIDIA is a leading technology company known for its work in graphics processing units (GPUs), accelerated computing, artificial intelligence, data center technologies, gaming, and professional visualization. Its platforms support applications ranging from AI model development and scientific computing to gaming graphics and autonomous systems. For quality engineering professionals, NVIDIA offers an environment where software reliability and performance are closely connected to hardware capabilities. Testing device drivers and SDKs can involve complex interactions among the operating system, hardware architecture, APIs, and development tools. This makes the role attractive to engineers who want to move beyond conventional web application testing.
📌 Job Overview
- Company: NVIDIA
- Job Title: Senior Tools Development Engineer
- Job Category: Quality Assurance / Test Automation / Tools Development
- Location: Pune, Maharashtra
- Experience Required: 5+ years
- Key Technologies: Python, C# or similar programming languages, AI-powered test automation, Windows OS, SQL, MongoDB, Elasticsearch
- Testing Focus: Device drivers, SDKs, automation frameworks, regression testing, and CI/CD
- Application Method: DM to Indrayani Pawar
💼 Key Responsibilities
- Developing AI-driven test automation: Build intelligent workflows that improve the execution, maintenance, and effectiveness of automated tests.
- Testing device drivers and SDKs: Create and execute tests for NVIDIA software components running on Windows.
- Building scalable tools: Develop reusable automation utilities, test libraries, and frameworks that support reliable testing at scale.
- Improving test coverage: Identify gaps in existing test suites and expand coverage across different configurations and scenarios.
- Debugging complex failures: Investigate unexpected behaviour, isolate root causes, and collaborate with development teams to resolve defects.
- Supporting CI/CD pipelines: Integrate automated tests into continuous integration workflows and improve regression feedback.
⭐ Why Is This Role Good for Experienced Professionals?
- Move beyond traditional QA: The role emphasizes tools development, programming, and system-level testing rather than only manual test execution.
- Explore AI-assisted testing: Engineers can contribute to AI-powered workflows that may improve test generation, analysis, and debugging efficiency.
- Gain driver and SDK testing exposure: Testing low-level software components provides valuable experience with operating systems, hardware interactions, and platform compatibility.
- Build engineering depth: Developing reusable frameworks strengthens software design, debugging, and maintainability skills.
🧠 Skills Required
- Programming: Strong Python or C# development, object-oriented programming, reusable code design, debugging, exception handling, regular expressions, and package management with tools such as pip.
- AI-powered QA: Practical experience applying AI technologies to test-case creation, automation workflows, failure analysis, or test optimization.
- Windows and system fundamentals: Windows OS behaviour, processes, services, logs, device drivers, and hardware–software interactions.
- Computer architecture: Understanding of GPU, CPU, SoC, x86, and ARM architectures. Knowledge of PCIe, USB, and SATA interfaces can be valuable.
- Databases and search: SQL, MongoDB, Elasticsearch, querying test results, and analysing large volumes of logs.
- Professional skills: Analytical thinking, technical communication, task estimation, prioritization, and collaboration with development teams.
🎯 NVIDIA Interview Rounds: What to Expect
- Round 1 – Recruiter or hiring-team screening: Discuss relevant experience, programming strengths, interest in NVIDIA, and availability for an onsite role.
- Round 2 – Programming and problem-solving: Expect preparation around Python or C#, OOP principles, data structures, exception handling, and writing maintainable code.
- Round 3 – Automation and framework design: Practise designing a reusable test framework, creating test utilities, integrating automated tests into CI/CD, and handling flaky tests.
- Round 4 – System-level testing and debugging: Prepare scenarios involving Windows drivers, SDK validation, hardware compatibility, logs, database queries, and root-cause analysis.
- Round 5 – Managerial or behavioral discussion:
💰 Expected Salary Range
NVIDIA has not disclosed the salary for this specific opening in the supplied announcement. Compensation may depend on experience, interview performance, level, and the overall offer structure. Third-party salary estimates can be inconsistent, so a specific range cannot be stated confidently as the official package. Candidates should confirm the expected fixed pay, variable compensation, stock or equity components, and benefits with the recruiter.
📚Preparation Tips
- Practise hardware-aware test scenarios: Explain how you would test a driver or SDK across different CPU/GPU configurations and operating-system setups.
- Strengthen Python fundamentals: Practise dictionaries, tuples, regular expressions, exception handling, file operations, logging, and clean OOP design.
- Prepare an AI-testing use case: Describe how AI could help generate test cases or analyse logs, and how you would prevent false positives, validate outputs, and protect sensitive data.
- Understand CI/CD: Explain how automated suites run in a pipeline, how failures are reported, and how flaky tests are separated from genuine product defects.
📄 Resume Tips for Applicants
- Lead with engineering impact: Highlight automation frameworks, internal tools, test utilities, and programming-heavy QA projects.
- Quantify results: Include measurable improvements in test execution time, coverage, defect detection, regression effort, and failure-triage time.
- Show relevant technical depth: Mention Python/C#, OOP, Windows testing, databases, AI-assisted QA, and CI/CD only where you have practical experience.
- Describe debugging ownership: Include examples where you isolated complex failures and helped drive defects to resolution.
- Keep projects specific: Explain the problem, your implementation, the technologies used, and the measurable outcome instead of listing tools without context.
📨 How to Apply
Interested candidates can DM to Indrayani Pawar and share updated resume if their experience matches the job requirements.
Final Thoughts: This opportunity is best suited to experienced QA and software automation engineers who enjoy programming, AI-assisted testing, system-level debugging, and building scalable tools. Candidates who can demonstrate strong coding fundamentals and explain how they investigate complex failures should focus their preparation on those strengths.
