How do you stay updated on the latest developments in low-level hardware components and associated tooling?
- Follow-up: Can you discuss a specific project where your low-level hardware knowledge was critical to the success of the task?
Tell me about your experience using SQL, PromQL, and Pandas for data analysis.
- Follow-up: Can you provide an example of a challenging data analysis task you completed?
Describe a situation where you had to monitor the outcome of deployed updates and how you ensured the desired outcome was achieved.
- Follow-up: What measures did you take to minimize disruption caused by hardware faults?
How do you approach developing reproducible analyses and building dashboards?
- Follow-up: Can you share an example of a project where you built effective visualizations to communicate complex hardware health data?
Tell me about your experience with maintaining hardware health tests.
- Follow-up: Can you provide an example of a scenario where you successfully root-caused and reproduced a hardware problem?
How would you design a new way of interacting with AI models and systems?
- Follow-up: How would you ensure a positive user experience in this new interaction method?
Can you provide an example of how you have translated the evaluation of AI models into meaningful messaging for users? How did you ensure the accuracy and clarity of the messaging?
- Follow-up: How did the users respond to the messaging? What impact did it have on their understanding and trust in the AI system?
How do you collaborate with cross-functional teams, such as product safety and policy, to make decisions on model behavior and data collection?
- Follow-up: How do you ensure effective communication and alignment in such collaborations?
What strategies do you employ to help humans develop trust in AI systems and oversee their decision-making?
- Follow-up: Can you share an experience where you successfully implemented these strategies?
How do you approach collecting and analyzing qualitative and quantitative data on how humans understand the capabilities of AI systems?
- Follow-up: Can you provide an example of a situation where you had to analyze such data and draw insights from it?
Can you provide an example of a problem you solved in your previous role that had a significant impact on accelerating research?
- Follow-up: What metrics or indicators did you use to measure the impact of your solution?
Describe a situation where you needed to collaborate with other teams to meet a challenging deadline. How did you ensure successful collaboration?
- Follow-up: What strategies did you employ to resolve conflicts or differences in approaches?
Imagine you are tasked with developing a new research tool to accelerate AI research. How would you approach this project?
- Follow-up: What features or functionalities would you prioritize in the tool?
Tell me about a challenging bug you encountered while writing machine learning code. How did you solve it?
- Follow-up: What steps did you take to ensure the bug didn't reoccur?
Can you describe a time when you had to optimize a large-scale AI model?
- Follow-up: How did your optimizations impact the overall performance of the model?
Describe a time when you improved infrastructure reliability in a previous role.
- Follow-up: How did your solution impact the overall performance?
Describe a situation where you had to quickly learn new knowledge to solve a problem.
- Follow-up: What strategies did you use to effectively acquire the necessary knowledge?
How would you optimize the utilization of AI models on a heterogeneous fleet?
- Follow-up: Have you dealt with challenges related to deploying AI models on diverse hardware?
Tell me about a time when you diagnosed and resolved performance bottlenecks.
- Follow-up: What tools or techniques did you use to identify the root cause?
How would you ensure the security and scalability of an inference infrastructure?
- Follow-up: Can you provide an example of a potential security or scalability issue and how you would address it?
