Build a Candidate Search System
Company: Mercor
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Build an end-to-end candidate search system for recruiting.
### Input
Each search request contains a job posting with:
- Job title
- Job description
- Hard criteria, such as required skills, location constraints, work authorization, minimum years of experience, education, or availability
You are given access to a candidate database. Each candidate profile may contain structured fields, such as location, skills, years of experience, and availability, plus unstructured text such as resumes, work history, project descriptions, and notes.
### Output
Return the `10` best-matching candidates for the job posting.
### Requirements
- Correctly enforce hard criteria whenever possible.
- Rank candidates by overall relevance to the job.
- Handle both structured filters and semantic matching over unstructured candidate text.
- Produce an end-to-end working implementation that can be evaluated by an automated endpoint.
- Use evaluation results to debug and iterate on the search quality.
- Be prepared to explain how you used AI tools, how you designed the retrieval and ranking pipeline, and how you diagnosed and fixed quality issues.
Quick Answer: This question evaluates competency in designing and implementing an end-to-end candidate search pipeline that enforces hard filters, performs semantic retrieval and ranking over unstructured text, and uses evaluation-driven iteration for quality improvement.