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Mercor is seeking an Assessment Designer & Learning Analyst in San Francisco. This role focuses on designing assessments to measure expert performance while analyzing the data to provide insights for continuous improvement. You'll partner closely with the Learning & Development team to ensure the effectiveness of assessments.
The ideal candidate holds a Master’s degree and has a solid background in quantitative analysis, assessment design, and educational measurement. Join Mercor to contribute to the AI economy.
Mercor's mission is to organize human intelligence to power the AI economy. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $3 million a day.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast‑paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in‑person five days a week in our San Francisco, NYC, or London offices.
We're looking for an Assessment Designer & Learning Analyst who can build rigorous measurement systems and use data to understand what actually drives expert performance.
This is not an instructional design role. You won't be building courses or writing training materials. You will be designing the assessments and certification frameworks that measure whether our talent experts and internal teams are genuinely skilled — and then doing the analytical work to understand what those assessments reveal, what predicts expert effectiveness, and how our programs should evolve based on evidence. You will be working closely with the Learning & Development team to understand the relationship between materials and assessments, and making recommendations to the team based on your analysis.
If you've come from an ed school background, taught in a high‑accountability environment, and completed quantitative projects or theses, and are energized by the measurement and data side of education — this role is for you.
Item-level analysis: difficulty index, discrimination index, inter‑rater reliability (Cohen's kappa, Krippendorff's alpha, ICC)
Assess and report on assessment validity and reliability — and know what to do when results look off
Analyze relationships between variables: correlation, regression, and basic predictive modeling
Work fluently in Excel or Google Sheets for data cleaning and summaries
Use Python, STATA or R for deeper analysis (basic proficiency expected; we'll grow this with you)
Translate quantitative findings into plain‑language recommendations for non‑technical stakeholders
We will ask you to demonstrate this. Finalists will complete a short take‑home exercise involving a real assessment dataset — you'll analyze item performance, identify problems, and recommend improvements.
The quality of AI systems depends on the quality of the humans who train them. Your job is to measure that quality rigorously, understand what drives it, and help Mercor build smarter systems for developing expert performance. It's a rare opportunity to apply serious measurement science at a company operating at the frontier of AI development — where the stakes for getting it right are unusually high.