NVIDIA DevOps / SRE Interview Questions

1.Describe a time when you needed to get buy-in from multiple engineering teams to adopt a new DevOps practice or tool. How did you approach the rollout?

2.Tell me about a time when you went above and beyond to solve a problem for an internal team or customer. What extra steps did you take?

3.Tell me about a time you had to coordinate with both hardware and software teams to resolve a critical production issue. What was the problem and how did you facilitate the collaboration?

4.Walk me through the most complex performance optimization problem you've solved involving GPU workloads. How did you diagnose the bottleneck and what was your solution?

5.Describe a situation where you had to deeply understand an internal customer's GPU workload requirements and redesign your infrastructure to meet their performance needs. What did you discover and implement?

6.Tell me about the riskiest infrastructure decision you made in the last two years. What was the technical bet, and how did you convince your team to move forward?

7.You're managing a Kubernetes cluster running hundreds of DGX nodes for our AI training workloads. The cluster starts exhibiting random pod failures and InfiniBand connectivity issues. How would you debug and fix this?

8.Write a Python script that monitors GPU memory fragmentation across a multi-GPU system and triggers memory defragmentation when fragmentation exceeds a threshold. Consider both CUDA memory management and the impact on running workloads.

9.Our Triton Inference Server deployment is hitting memory limits when serving large language models. The models are 70B parameters and we're seeing OOM errors during peak traffic. What's your approach to optimize this?

10.Design a monitoring and alerting system for a fleet of autonomous vehicle compute platforms running NVIDIA Drive. What metrics would you track and how would you handle data collection from vehicles in the field?

11.You need to implement CI/CD for CUDA kernel development where engineers are pushing optimized kernels daily. The pipeline needs to validate performance regressions across different GPU architectures. How would you design this?

12.Our Omniverse collaboration platform is experiencing intermittent sync failures between distributed teams working on large 3D scenes. The failures seem correlated with scene complexity and team size. How would you diagnose and architect a solution?

13.Design a deployment pipeline for CUDA kernels that supports thousands of GeForce and RTX GPU variants. How would you ensure performance regression testing across this hardware matrix?

14.Our NVIDIA DGX clusters are running multi-node AI training jobs that can take weeks to complete. Design a checkpoint and recovery system that minimizes training time loss during hardware failures.

15.Design an auto-scaling system for TensorRT inference workloads where model loading takes 2-3 minutes and GPU memory varies dramatically by model size. How do you balance cold start times with resource efficiency?

16.Build a global asset synchronization system for NVIDIA Omniverse where 3D scene files can be 50GB+ and teams across continents need real-time collaboration. How do you handle the scale and latency challenges?

17.Design a telemetry and diagnostics platform for NVIDIA Drive systems deployed in autonomous vehicle fleets. The system needs to collect performance data, detect anomalies, and push critical updates while vehicles are in motion across cellular networks with varying reliability.

18.Build a resource allocation system for shared GPU clusters where different teams run everything from small research experiments to massive distributed training jobs. The system needs to ensure fair access while maximizing overall cluster utilization across workloads with vastly different GPU memory and compute requirements.

19.Tell me about a time you had to push back on a product or engineering team's timeline that you knew would compromise system reliability. How did you handle the pushback?

20.Describe a time when you had to learn a completely new technology domain to solve a critical problem. What was your approach and what did you discover?

21.Tell me about a time you influenced a senior engineer or architect to change their technical approach when you didn't have direct authority over them. What was the situation and outcome?

22.Walk me through a time when you took ownership of a failing project or system that had multiple teams pointing fingers at each other. How did you turn it around?

23.Describe the most ambitious technical vision you've championed that others initially thought was too risky or complex. How did you build support and what was the result?

24.Tell me about a time when you had to rapidly adapt your team's infrastructure approach due to unexpected customer demands or market changes. What decisions did you make and how fast did you move?

25.Estimate how many CUDA cores NVIDIA ships globally per year across all our GeForce, RTX, and datacenter GPU product lines. Walk me through your calculation framework.

26.Our GeForce NOW cloud gaming service is experiencing 15% higher GPU utilization than usual, but user complaints about lag have tripled. How would you diagnose what's driving this disconnect?

27.Estimate the annual infrastructure cost to run inference for ChatGPT-scale models if every NVIDIA RTX GPU owner could run local inference. How would this compare to our current datacenter approach?

28.Our TensorRT optimization pipeline takes 6 hours to process and validate a new model deployment. Product teams want this down to 30 minutes. What's your framework for approaching this 12x performance improvement?

29.NVIDIA's autonomous vehicle partners report that Drive platform updates are causing 2-day fleet downtime for testing and validation. Estimate the economic impact of this delay and design a framework to reduce it to 4 hours.

30.Estimate how much revenue NVIDIA loses per hour when our CUDA developer portal experiences downtime. Factor in the global developer ecosystem and build a framework for calculating acceptable SLA targets.