Academic & PhD Experts: $70-$200/hr from Frontier AI Labs

Open application· 92 spots this round

$70-$200/hr academic and research work, on your schedule

Review AI-generated research, proofs, derivations, and literature claims the way you'd referee a submission. Catch the unsupported leap, the misattributed citation, the result that doesn't replicate. Deep expertise in a narrow field - the kind only years in it produce - is exactly the signal frontier labs are paying to capture.

Fully remoteYour scheduleWeekly pay

Apply nowApply once, get matched on a rolling basis. No prior AI experience needed.

Trusted by top research companies

Rated by experts worldwide

Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.

Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.

Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need a referee's eye on an overstated result spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.

That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.

We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.

If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.

Zac & Jack

Founders

Academia & Research questions

Still curious? Write to us at support@terac.com.

Narrow specializations are often exactly what frontier model developers need most, because gaps in model reasoning tend to cluster in highly technical subfields where training data is sparse. Evaluators working in areas like category theory, archaeogenomics, or pre-modern philology are actively sought to stress-test outputs that general reviewers cannot meaningfully assess. You do not need to cover a broad domain to contribute valuable work.

Eligibility is based on your demonstrated expertise in the relevant field, not on the Carnegie classification of your degree-granting institution. PhDs from internationally accredited programs, including those outside the United States, qualify provided your work history or publications reflect genuine domain depth. Postdoctoral experience and a strong publication record can carry as much weight as the institutional name on your diploma.

The work involves evaluating and improving AI reasoning, not producing materials intended for academic submission by students. Typical tasks include annotating model-generated research explanations, identifying flawed citations or statistical reasoning, and writing worked examples that demonstrate expert problem-solving for model training. If a specific task raises an institutional conflict-of-interest concern, you can flag it and it will not be assigned to you.

Tasks typically involve reviewing AI-generated literature summaries, experimental design critiques, statistical interpretations, and domain-specific reasoning chains rather than raw primary sources. You are not expected to access paywalled databases on your own or use institutional library credentials for this work. The content provided to you will be self-contained, and your role is to evaluate the quality of the AI output against your own expert knowledge.

Interdisciplinary profiles are treated as a distinct strength, particularly for evaluating AI outputs that involve multi-step reasoning across domain boundaries, such as applying Bayesian inference to behavioral data or interpreting genomic results in a clinical context. You can list both areas of expertise in your profile and will be matched to tasks that require exactly that combination. Pure disciplinary specialists and interdisciplinary researchers are both in demand, serving different evaluation needs.

Why your expertise matters

Academic researchers and PhD-level scientists reason across disciplines the way frontier models struggle most: synthesizing conflicting evidence, designing studies with proper controls, and recognizing when a citation is being misrepresented. AI systems trained on published literature inherit the biases, retraction problems, and disciplinary blind spots of that corpus, and only a practicing researcher can identify when a model's statistical reasoning, experimental design, or literature review crosses from plausible-sounding to actually wrong. Your judgment on peer-review-quality errors is the signal that separates a model that passes surface-level checks from one that a grant committee would trust.

How pay works

Rates toward the $200/hr ceiling go to experts in fields where the verification burden is highest: computational biology, econometrics, climate modeling, or any domain where evaluating one AI output requires working knowledge of a specific software toolchain (Stata, MATLAB, R with domain-specific packages, GAMS) and familiarity with the methodology standards of a specific journal tier. Work is fully remote, billed hourly, and released on verified completion of each task batch, so you are never waiting on a client's approval cycle to get paid.

What the work looks like

A sample of the academic and research work you would pick up. Every project is scoped, remote, and paid on verified completion.

Evaluate an AI-generated literature review section for a methods paper, flagging misattributed findings, omitted contradictory studies, and claims unsupported by the cited sources.
Write a worked example of a difference-in-differences analysis from scratch, narrating each modeling decision so the model learns how an econometrician reasons through parallel-trends testing.
Review an AI-produced research proposal for a hypothetical NIH R01 submission, identifying weaknesses in the specific aims logic, power calculations, or justification of the primary outcome measure.
Score a set of AI-generated experimental design descriptions on internal validity, rating each threat to inference (selection bias, attrition, contamination) and explaining your reasoning.
Draft a multi-turn conversational prompt-and-response pair that demonstrates how an expert handles a student asking about p-hacking, modeling the distinction between exploratory and confirmatory analysis.
Stress-test an AI-summarized Cochrane-style systematic review by checking whether PICO criteria were applied correctly and whether the pooled effect estimate is consistent with the individual studies cited.