NVIDIA's interview process typically starts with a resume screening, where candidates must showcase relevant experience and skills. Following the initial review, selected applicants are invited to complete Online Assessments (OAs), focusing on coding and problem-solving capabilities. This phase is crucial for advancing in the process.
Candidates who perform well in the OAs may then face one or two phone screens before being called onsite for further assessment. The onsite rounds are known for their complexity, often involving multiple technical interviews with a mix of coding exercises, system design, and discussions about past projects. This stage tests both technical expertise and cultural fit.
At NVIDIA, the resume screening process is automated using AI algorithms, focusing on relevant experience and technical skills. Around 75% of applicants get filtered out at this preliminary stage.
For those advancing, NVIDIA typically conducts Online Assessments (OA) focusing on coding and problem-solving skills. Top performers in the OA are then invited for preliminary screening calls.
NVIDIA typically conducts one or two phone screens for software engineering candidates. These preliminary calls focus on assessing technical skills and cultural fit, involving both coding questions and discussions about past projects and experiences.
The phone screenings are designed to be concise, usually lasting about 45 minutes each. Candidates might be asked to solve programming problems or to explain complex algorithms, reflecting the rigorous standards expected by NVIDIA in their tech workforce.
NVIDIA typically conducts multiple rounds in their onsite interviews, generally encompassing four to six sessions. These interviews are designed to evaluate coding skills, system design abilities, and behavioral aspects to ensure a well-rounded candidate assessment.
Each onsite visit usually includes at least one system design interview, a couple of coding tests, and one or two behavioral interviews. The session might also integrate tasks specific to the job role, such as debugging or optimization problems. Interviews are both one-on-one and panel setups, aiming to gauge deep technical knowledge and cultural fit.
At NVIDIA, after completing the interview rounds, candidates may undergo team matching and final discussions with managerial staff. There's also an opportunity to negotiate job offers before finalizing the employment terms.
NVIDIA's interview process is similar to the typical coding interview but tends to focus more on easier and medium difficulty problems, especially those involving Two Pointers and basic data structures and algorithms (DSA). These problems are generally feasible to walk through during an interview, making the process more approachable. The coding challenges at NVIDIA are typically easier than those encountered at FAANG companies, providing candidates with a less daunting experience.
| Problem + Solution | Patterns | Difficulty |
|---|---|---|
| Last Stone Weight | Heap | Easy |
| Degree of an Array | Basic DSA | Easy |
| Minimum Operations to Reduce an Integer to 0 | Dynamic Programming, Misc. | Medium |
| LRU Cache | Basic DSA | Medium |
| Ways to Make a Fair Array | Two Pointers | Medium |
| Verify Preorder Sequence in Binary Search Tree | Backtracking, Binary Search, Misc. | Medium |
| Design Circular Queue | Basic DSA | Medium |
| Minimum Knight Moves | Breadth-First Search | Medium |
| Range Sum Query 2D - Immutable | Two Pointers | Medium |
| Reverse Bits | Misc. | Easy |
Describe a time when you had to adapt to a significant change in a project. How did you handle the transition?
Tell me about a time when you improved a process or made a task more efficient.
Can you give an example of how you've worked effectively under pressure?
Describe a time when you had to collaborate with others on a project. What was your role, and how did you ensure the success of the team?
Tell us about a situation where you and your team disagreed on a solution. How did you handle the discussion and what was the outcome?
NVIDIA is at the forefront of AI and gaming technology. Can you describe a collaborative project you have undertaken that involved similar technologies?
Describe a project where you had to use both your coding skills and your knowledge of hardware optimization.
How have you ensured your code is both efficient and scalable in past projects?
NVIDIA is at the forefront of AI and gaming technology. Can you discuss any experience you have working with AI models or gaming technology that would be relevant to our projects?