For domain experts

Your judgment can help
shape what AI learns.

Bring subject knowledge, practical experience, and careful reasoning to the questions that require more than a surface-level answer.

Express your interest

The contribution is in
what you can see.

An expert often notices what a general reader misses: an assumption that changes a conclusion, an edge case that breaks a solution, or a plausible explanation unsupported by its evidence. Making those distinctions explicit can provide useful human feedback for frontier AI training.

Datlore brings vetted domain experts together with task environments and reward-model tooling. Expert contributions sit within that learning loop, connecting informed judgment to the tasks and episodes AI labs use in their research.

Relevant experience depends on the problem. Science, software, mathematics, and professional reasoning are examples of areas where specialist knowledge may matter. They are illustrative domains, not a promise of current openings or a complete list of work. Describe your actual depth and the kinds of decisions you are equipped to assess.

What a contribution could involve.

Make comparisons meaningful.

Review alternative responses against a brief and explain which better meets the stated criteria. The important contribution may be identifying why a polished answer fails, or why neither option deserves approval. A preference becomes more useful when the decisive reason is visible.

Explain a consequential error.

Locate a faulty inference, missing condition, or unsupported claim. Connect it to the supplied evidence and distinguish a substantive failure from a stylistic difference. In an illustrative software task, this might mean explaining why a patch misses an edge case rather than rewriting an entire program.

Help define a good task.

Assess whether instructions actually test the intended capability. Domain knowledge can reveal when a task is too ambiguous, relies on unavailable information, or rewards a shortcut. A carefully chosen near miss can clarify a rubric more effectively than a long abstract definition.

Review a sequence of decisions.

For an episode, examine the evidence available at each step and whether the chosen action was justified. A final outcome does not necessarily establish good reasoning. Review can separate a sound decision under uncertainty from a favorable result reached for the wrong reasons.

These are illustrative task types. Any actual engagement requires separately established scope and terms.

Careful work starts
with clear expectations.

When introducing your experience, distinguish familiarity from depth. Describe a type of problem you can assess, the evidence you normally use, and a consequential error you know how to recognize. You can do this without naming a client or sharing a private example. A focused account helps connect your knowledge to an appropriate review question.

Useful feedback should be specific, grounded in the permitted evidence, and responsive to the task. Follow the brief while flagging ambiguities that prevent a defensible assessment. Do not fill gaps with invented facts or treat an uncertain question as settled merely because a form expects an answer.

Communicate the boundary of your expertise. A narrow, well-supported judgment is more useful than a confident assessment outside your experience. If two interpretations are reasonable, explain what additional instruction would resolve the difference.

Protect information you are not entitled to share. An expression of interest does not require client materials, confidential research, personal records, or proprietary examples. A high-level description of your subject knowledge and work is enough for an initial introduction.

Expressing interest does not guarantee acceptance, a project, compensation, a particular schedule, or ongoing work. Any requirements, review process, responsibilities, and commercial terms would need to be discussed separately before an engagement begins.

For a practical view of strong review, read What makes expert feedback useful? To understand the broader context, explore the Datlore platform.

Expert interest

Tell us where
your expertise runs deep.

Share your domain, relevant experience, and the kinds of problems you assess. A short, specific introduction is more useful than a broad list of interests.

Use this form for an initial expression of interest. For account-access questions, visit access support.