$60–80/hr · Mercor · Part time
An HR professional who trains and evaluates AI systems on recruitment, payroll, and compliance tasks by creating realistic scenarios and providing domain-specific feedback.
What you would do
- Evaluate AI model outputs on HR and administration tasks, assessing correctness, compliance, and practical feasibility
- Design realistic scenarios based on talent acquisition, benefits administration, payroll, and compliance workflows
- Create training materials and test cases grounded in actual HR decisions and edge cases
- Provide detailed feedback to AI research teams on how models handle HR complexity and regulation
- Collaborate remotely to refine AI training approaches based on domain expertise
Who they want
- Professional experience in talent acquisition and onboarding, payroll and benefits, or HR policy and employment compliance
- Strong written and verbal communication skills for remote collaboration with technical teams
- Ability to work independently on projects with flexible scheduling and async communication
- Comfort explaining HR decisions and operational constraints to non-HR professionals
- Willingness to contribute 15-30 hours per week on rolling project basis
Main skills
What the interview asks about
1.Recruitment and hiring complexity
AI models for talent acquisition must handle regulatory constraints, equity considerations, and practical hiring stages; missing nuance creates compliance risk.
For example: “A candidate has required skills but needs visa sponsorship, delaying onboarding. Should an AI recommend them? How do sponsorship, budget, and timeline constraints interact?”
2.Payroll and benefits accuracy
Payroll errors compound rapidly and affect employee trust and legal compliance; AI systems must respect tax codes, contribution limits, and deduction sequencing.
For example: “An employee contributes 25% to 401k (max $23,500). Base is $100,000; bonus $40,000 in December. How should payroll calculate limits, and what mistake would an AI commonly make?”
3.Employment law and policy application
HR decisions live in a landscape of labor law, FMLA, ADA, and company policy; misinterpretation exposes employers to liability.
For example: “An employee requests extended leave potentially qualifying for FMLA, but policy says PTO rolls over while federal law says FMLA is unpaid. How would you instruct an AI to handle this?”
4.Onboarding and employee lifecycle
Onboarding workflows involve identity verification, compliance documentation, benefits enrollment, and system access - failure at any step delays productivity or creates audit gaps.
For example: “An offer is contingent on background check at day 45 with no update. Policy allows starting after 30 days, but some roles need clearance. Design a scenario teaching an AI when to escalate.”
5.Domain feedback for model development
AI researchers need to understand not just what HR decisions are correct, but why - the constraints, regulations, and stakeholder considerations that shape real-world HR logic.
For example: “An AI suggests uniform PTO policies across roles. But executives negotiate retention bonuses tied to PTO, and contract employees differ. How would you frame this feedback for researchers?”
A task you may get
Evaluate 3-5 AI-generated HR decisions (hiring, payroll, policy) for correctness, compliance risk, and feasibility. Flag errors, explain the underlying principle, and suggest improvements.
How to prepare
- Document a real hiring process you've participated in, including screening criteria, offer letter logic, and edge cases where judgment mattered
- Study one payroll, benefits, or compliance topic deeply (FMLA, tax withholding, retirement plan rules) to understand where AI errors create downstream problems
- Prepare 2-3 examples of HR scenarios where policy, law, and business constraints collide - how did your organization resolve the ambiguity?
- Review a non-HR technical role or research paper to practice explaining HR complexity to someone unfamiliar with HR operations
The facts
- Pay
- $60–80/hr
- Hours
- Part time
- Where
- Remote
- Field
- Business Operations
- Role type
- Talent network
- Posted
- 2/27/2026
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