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A leading AI research organization is seeking advanced LLM power users with strong experience using MCP and (more importantly) plugins/connectors for real-world personal life tasks.
This project focuses on evaluating how well AI systems handle personalized, multi-step life tasks that require context, judgment, planning, and use of connected tools such as Google Drive, Expedia, Notion, and other plugins/connectors.
This role is ideal for people who use AI heavily in their personal lives and can do a better job replicating what AI could do if it weren’t available
You will help evaluate AI systems on complex personal workflows, including tasks across:
Responsibilities may include:
Strong candidates will have:
Extensive rubric experience is especially valuable, including 100+ hours on prior rubric projects involving rubric design, evaluation, and quality assessment.
The strongest candidates are LLM power users who are already using plug-in tools in their personal lives for high-context tasks such as trip planning, health research, home services, food and dining decisions, career planning, personal organization, or similar workflows.
Candidates who want to be more competitive for this and future opportunities are encouraged to proactively spend time learning MCP and using LLM plugins/connectors before applying.
LLMs are quickly becoming personal assistants for everyday decisions, but truly useful AI needs to do more than produce generic advice. It needs to understand context, preferences, constraints, tradeoffs, and what success looks like in real life.
Your evaluations will help improve how AI systems support people with practical, high-context tasks across food, health, travel, productivity, careers, and life organization. This work directly contributes to making AI assistants more personalized, trustworthy, and useful for real-world personal workflows.
We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Mercor partners with leading AI labs and enterprises to train frontier models using human expertise. You will work on projects that focus on training and enhancing AI systems. You will be paid competitively, collaborate with leading researchers, and help shape the next generation of AI systems in your area of expertise.