Physics Expert (PhD)

$80–90/hrrate as listed

micro1 contracts physics PhDs to write, review, and grade advanced physics problems and model responses for a customer's AI training program. Work is remote and output-based, with observed rates in the $80–90/hr range and a weekly minimum task submission.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

You will spend most of your time producing and adjudicating graduate-level physics content. In practice that means authoring problems that a strong model cannot brute-force, writing complete worked solutions with the reasoning made explicit, and reviewing model outputs to identify where the physics — not just the arithmetic — goes wrong. Expect to work across classical mechanics, E&M, statistical mechanics, quantum mechanics, and whatever applied subfield your dissertation lives in.

Draft original problem sets with unambiguous statements, stated assumptions, and verifiable final answers
Grade or rank model responses, citing the specific step where a derivation breaks or a limit is misapplied
Rewrite flawed items rather than rejecting them, and flag systematic gaps in the task pool
Occasionally record or write plain-language explanations of a phenomenon for downstream annotation use

What the platform screens for

micro1 runs an AI-led interview before any human review. It probes for a real doctorate and a real research trail — advisor, institution, thesis topic, publications — then pushes on depth with follow-up questions inside your stated specialty. Screeners also test written clarity and calibration: whether you can say precisely why an answer is wrong, and whether you distinguish a sign error from a conceptual failure. No prior AI or annotation experience is required, but sloppy notation and hand-waving read as disqualifying.

Logistics

Fully remote and asynchronous, with no fixed hours. Compensation is per accepted task rather than hourly, so effective rate depends on how quickly you draft and how often work clears QA; the $80–90/hr band reflects observed reports and is not guaranteed. There is a weekly minimum submission volume, and micro1 typically moves from screen to first tasks within 48 hours, so apply when you can actually start.

What they look for

Advanced physics knowledge
scientific data analysis
technical writing
verbal comms
analytical thinking

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you hold a doctorate (PhD) in Physics from an accredited institution?
PhD in Physics · required
Can you demonstrate expertise in both theoretical and applied physics beyond introductory coursework or teaching?
Advanced Physics Knowledge · required
Are you able to start on your first assigned tasks within 24–48 hours of completing onboarding?
Remote Work Availability · required
Can you commit to meeting the minimum weekly task submission requirements for this output-based role?
Weekly Output Commitment · required
Have you created, reviewed, or developed educational or technical physics materials (problem sets, explanations, courseware, or similar)?
Technical Communication Experience · preferred

0 of 5 answered

Included with this listing

Interview prep — 12 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether your doctoral work spans theoretical and applied physics as stated in preferred qualifications, and whether you can articulate your research with enough precision that we can map it to physics training data needs.
A strong answer: Name your PhD institution and thesis topic, then identify 2–3 core physics subfields (quantum mechanics, electromagnetism, thermodynamics, etc.) where your research or coursework was substantial. Show you can distinguish between breadth (what you studied) and depth (where you can evaluate and generate expert-level content).
Where people lose it: Vague summary like 'I studied physics' without naming your actual research area, institution, or the subfields you'd be evaluating content in. The screener needs to know whether you're qualified to judge advanced problem sets in your stated domain.