AI Training Roles
Português Get alertsAlerts
Menu
Menu
Jobs1687 Platforms Fields Languages Guides Remote by countryInterview prepPay reportToolsGlossary Get alerts
Português

Physics AI Training Interview Questions

Physics roles in AI training look for PhD or postdoc physicists, in areas such as statistical physics, quantum information, condensed matter and optical properties of materials, as named in current postings. One posting describes adaptive, spoken AI interviews that ask for concrete examples from shareable research. Expect to show depth and clear explanation.

10 open roles in this groupUpdated 9 October 2026

Practice questions

These are practice questions written for this site to help you prepare. No platform has said it asks these exact questions.

  1. Write a graduate-level problem in your sub-field with a unique numerical answer.

    A good answer covers: Clear setup, all constants given or standard, one answer with units, and a step-by-step solution.

  2. A model derives a result but drops a factor of 2 midway. How do you report it?

    A good answer covers: Identify the exact step, explain the origin of the factor, and give the corrected result.

  3. Explain the difference between a pure and a mixed quantum state.

    A good answer covers: Pure states are described by a single state vector; mixed states need a density matrix representing a statistical mixture. Mention the purity test.

  4. How do you check a result by dimensional analysis and limiting cases?

    A good answer covers: Units must match; the result should reduce to known answers in simple limits (zero coupling, high temperature).

  5. Describe a phase transition and an order parameter to a non-physicist.

    A good answer covers: Plain example (water freezing, magnet), and what quantity changes at the transition.

  6. How would you judge a model's explanation of an optical property of a material?

    A good answer covers: Check the physical mechanism, the equations, the approximations and whether the numbers are realistic.

  7. What makes a physics problem too easy for current models?

    A good answer covers: Plug-in formulas, textbook setups and single-step reasoning.

  8. What is your research area, and where would you not trust your own judgement?

    A good answer covers: Honest scope of expertise.

What the platforms say about the assessment

In the 2026-10-09 data, roles in this group came from SME Careers, micro1 and Meridial. Each platform runs its own process, and steps can differ by role. Below is what each platform's public pages or postings say, followed by lines quoted from current postings in this group.

SME Careers

Sources: sme.careers, api.sme.careers

micro1

Source: www.micro1.ai

Meridial

Source: boards-api.greenhouse.io

Quoted from current postings

"Participate in adaptive, spoken AI interviews, responding to one question at a time and providing clear, concrete examples from legally shareable research or projects."

Physics Expert PhD (AMO / Optical Properties of Materials), micro1

"Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter."

Physics Quality Assurance Lead (QAL), sme

Steps were read from public pages and postings, not tested first-hand. Passing a screening does not guarantee a project, hours or pay.

Terms to know

Stump the model
A task where you write a hard question the model gets wrong and record the correct answer, often used in expert STEM and coding projects.
Chain of thought
The step-by-step reasoning a model writes before its final answer. Some tasks ask you to check each step, not only the result.
Process supervision
Judging each step of a model's reasoning rather than only the final answer, common in maths, science and coding work.
Ground truth
The correct answer or label that model output is compared against.

Open roles in this group (10)

Sources

Other role groups