Computer Science Expert (PhD)

$80–90/hrrate as listed

micro1 contracts computer science PhDs to write and review advanced technical answers used as training and benchmark data for AI models. Work is remote, output-based, and paid per accepted task, with observed rates in the $80–90/hr range depending on throughput.

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 answer hard computer science questions the way a careful reviewer would want them answered: correct, complete, and explained well enough that a reader can follow the reasoning. Some tasks ask you to produce a "golden response" — a reference answer that later model outputs are measured against. Others ask you to audit existing questions and solutions for correctness, missing cases, outdated practice, or sloppy notation, and to document why a given answer is right rather than simply marking it so.

Subject matter spans the field: algorithms and complexity, systems and operating systems, distributed computing, databases and query optimization, networking, security and cryptography, programming languages and type theory, and ML/AI foundations. You will not be equally strong across all of it, and that is expected — micro1 routes tasks toward declared specialties. What matters is knowing where your expertise ends and flagging ambiguity instead of guessing.

What the screening looks for

A verifiable PhD in computer science or an adjacent field, with a research area you can describe in specific terms.
Depth under follow-up. The AI-led interview will push past your first answer — asking for a proof sketch, a counterexample, or the failure mode of a design you just endorsed.
Evaluation judgment. Can you tell a wrong answer from a right answer that is merely stated badly? Can you defend a correctness call in writing?
Written precision. Vague, hedged, or LLM-flavored prose is the most common reason strong candidates are declined.

Logistics

Fully remote and asynchronous, with occasional optional workshops or calls with annotation leads. Compensation is per accepted task, not hourly, so effective rate depends on how fast you work and how often your submissions clear review; the $80–90/hr band reflects what contributors have reported, not a guarantee. There is a weekly minimum task quota. micro1 moves fast — roles often fill within 48 hours, and selected experts are expected to submit first tasks within a day or two of onboarding. Most contributors treat this as 5–20 hours per week alongside other work.

What they look for

Computer Science
PhD
Python
Data Science

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 PhD in Computer Science, or in a closely related field such as Mathematics, Electrical Engineering, or a discipline whose graduate curriculum included advanced theoretical computer science?
PhD in Computer Science or closely related field · required
Have you authored, co-authored, or formally reviewed technical or educational computer science material (peer-reviewed research article, textbook chapter, curriculum, industry report, conference paper, or published peer review)?
Published or professional technical writing in CS · required
Are you able to begin onboarding and submit your first tasks within 48 hours of acceptance?
Immediate availability to start · required
Do you have a reliable internet connection, a quiet workspace, and the ability to maintain a regular weekly output schedule over at least a 3-month period?
Sustained remote work capacity · preferred

0 of 4 answered

Included with this listing

Interview prep — 10 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 you can articulate pedagogical choices and audience awareness when explaining difficult computer science topics, not just whether you've published.
A strong answer: Name a specific paper, curriculum section, or review you authored; explain a deliberate choice in how you structured or simplified a concept (e.g., which abstraction level you chose, which prerequisite knowledge you assumed, how you validated clarity with peers or users), and reflect on what feedback or revision made it work better.
Where people lose it: Listing publications without describing *how* you explained anything, or claiming you 'made it clear' without showing the actual reasoning behind your pedagogical decisions.