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Research Project Deep Dive and Your AI-Agent Research Workflow

NVIDIAApplied Scientist · Onsite
MediumApplied ScientistOnsiteBehavioral & Leadership

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Present a 20-minute deep dive into one research project covering motivation, research question, training pipeline, results and takeaways, then extend it to the internship problem. The round also asks how AI agents fit into your daily research workflow for writing, experiments and ideation, testing technical depth, honesty about results, and verification habits.

This is a 45-minute technical phone interview for a research internship. After about 10 minutes of introductions on both sides, the interviewer spends about 20 minutes on a deep dive into one of your research projects. Then they ask extension questions tied to the problem the intern would work on, and finally they ask how your day-to-day research workflow looks now that AI agents are available: writing papers, running experiments, and coming up with ideas. Treat the last question as a real probe of how you work, not as small talk.

Clarifying Questions

  • Should the deep dive cover the project you consider strongest, or the one closest to the team's work?
  • How technical should it be: loss functions, training details and ablations, or a higher-level narrative with the option to go deeper?
  • What problem would the intern work on, and what does the team's current approach to it look like?

Part 1 — Project deep dive

Pick one project and walk the interviewer through its motivation, the research question, the training pipeline, the experimental results, and the major takeaways, in about 20 minutes. Expect to be interrupted and asked to go deeper on any of these.

What This Part Should Cover

  • A motivation and research question that are precise and show why the problem matters
  • A training pipeline whose design choices are justified, not just listed
  • Results against baselines, with ablations, variance and honest limitations
  • Takeaways that go beyond this project

Part 2 — Extending your work to the internship problem

The interviewer describes the problem the intern would work on and asks how your research would extend to it, or what you would try first.

What This Part Should Cover

  • Questions that pin down the team's problem, data and success metric
  • A specific mapping from your methods and lessons to that problem
  • A scoped first experiment with a success criterion, risks and a fallback

Part 3 — Research workflow with AI agents

Describe your day-to-day research workflow now that agents exist: how you use them to write papers, run experiments, and come up with ideas, and where you do not use them.

What This Part Should Cover

  • Concrete uses across writing, experiments and ideation
  • Verification habits that protect correctness and reproducibility
  • Limits and failure modes of agents in research, and integrity rules such as citations and disclosure
  • An opinion on how automation changes what a researcher spends time on

Hints

Hint 1

One question, one thread: Choose the single question your project answers and make every later section (pipeline, experiments, results) point back to it; know the numbers you will be asked to defend.

Hint 2

Transfer, change, test: Separate what carries over from your project (methods, infrastructure, lessons) from what would have to change, then name a first experiment and how you would judge it.

Hint 3

Delegate, verify, and know where it breaks: For each activity, be concrete about what you hand to an agent, how you check the output, and a failure you have seen or would expect.

Follow-up questions

  • What was the most important idea in this project that failed, and how did it change the direction of the work?
  • With ten times the compute or data, what would you do differently, and what would you expect to learn?
  • Which part of your research workflow would you never hand to an agent, and why?
  • How would you measure whether agents actually make you a more productive researcher, rather than just faster at producing text and code?