# AI interview prep for physics majors

Physics graduates fork into three fairly different interviews: research roles that dig into your specific lab or thesis work, engineering roles that test applied problem-solving, and quantitative roles in finance or data that lean on estimation and modeling under time pressure. Intervieux's technical and resume-based AI interviews mirror that fork, so you can rehearse an estimation problem or a project walkthrough, whichever your target track actually expects.

A physics degree signals general problem-solving ability more than one specific job, which means the interview usually has to prove that ability concretely rather than assume it. Research interviews ask you to defend a specific project. Quantitative and finance interviews often include an estimation question with no clean answer, testing how you structure an unfamiliar problem out loud, not whether you get the exact number right. Engineering-adjacent interviews sit somewhere between the two, mixing applied technical questions with a project walkthrough closer to what a mechanical or electrical engineering interview looks like.

## Where a physics degree actually leads

A meaningful share of physics graduates go on to research-science roles, staying in a lab as a research assistant or moving toward a PhD, often via a postdoc-adjacent research position first. A second group moves into engineering-other roles, applying physics training to applied problems at hardware, aerospace, or instrumentation companies. A third group, often the largest outside academia, moves into data-analytics or quantitative finance roles, where the same modeling and estimation skills that show up in physics coursework translate directly into pricing, risk, or data science work.

## What a first physics interview actually looks like

Research interviews ask about your thesis or lab project in real detail: what you measured, what broke, how you'd redesign the experiment with more time. Quantitative and finance interviews lean on estimation questions, like sizing an unfamiliar market or reasoning through a probability problem out loud, since interviewers care more about your reasoning process than a clean final answer. Engineering-adjacent interviews mix a project walkthrough with applied technical questions specific to the hardware or system you'd be working on. Across all three, interviewers ask you to reason from first principles on the spot, which is the one habit that carries across every version of this interview regardless of the specific role.

## Which interview types to practice

The technical interview type is the strongest match for quantitative and estimation-style questions, since it pushes you to reason through a problem rather than recall a fact. The resume-based type works best for research-track interviews, where your specific thesis or lab project carries most of the conversation. The job-description-based type is worth adding once you know which of the three tracks you're targeting, since a hardware engineering posting and a quantitative analyst posting will pull very different follow-up questions from the same physics background. Quantitative interviews sometimes follow an estimation question with a harder version of the same problem once you've answered, checking whether your reasoning holds up under a changed assumption rather than only the first pass.

## Frequently asked questions

### Do physics interviews always include estimation questions?

Not always, but they're common for quantitative and finance-adjacent roles, where an interviewer wants to see how you structure an unfamiliar problem out loud. Research and engineering interviews are more likely to ask about a specific project instead.

### How much does my thesis matter in a research interview?

A lot. Research interviews usually spend real time on your thesis or lab project, asking what you measured, what didn't work, and what you'd change, so it's worth being able to walk through it clearly to someone outside your subfield.

### What's the difference between an engineering-adjacent and a quantitative finance interview?

Engineering-adjacent interviews mix a project walkthrough with applied technical questions about a specific system or hardware. Quantitative finance interviews lean more on estimation and probability reasoning, often with a case that has no single correct answer.

### Which interview type should I use to practice?

Use the technical type for estimation and quantitative reasoning practice, and the resume-based type if you want an interviewer to dig into your specific thesis or research project the way a real panel would. Most candidates benefit from both, since few employers test only one side of the fork.

## Related pages

- [Statistics interview prep](/interview-prep/statistics)
- [Robotics interview prep](/interview-prep/robotics)
- [Browse job openings](/jobs)
- [Frequently asked questions](/faq)

## Practice an estimation or research interview

Run a technical or resume-based AI interview to rehearse reasoning through an unfamiliar problem or defending your research project.

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