Katkat
Back

Build a Candidate Search System

MercorMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

Last updated

Design an end-to-end talent search and matching engine that combines semantic vector retrieval with structured filtering to handle unstructured resumes, ambiguous queries, and strict hiring constraints.

Scenario

Design an AI-powered candidate search and recommendation system for a modern tech recruiting platform. Recruiters input free-form search queries (e.g., "Senior ML engineer with experience scaling LLM training pipelines in PyTorch") or upload an entire job description (JD). The system must surface the most relevant candidates from a database of millions of profiles, balancing semantic relevance with strict hiring filters (e.g., work authorization, minimum years of experience, geographic timezone).

Candidate profiles are compiled from noisy, unstructured resume text, GitHub/portfolio links, and self-reported survey data, leading to missing attributes and non-standard job titles (e.g., "AI Architect" vs. "Staff Machine Learning Engineer").

Requirements

Functional Requirements

  • Hybrid Query Input: Support both short keyword/natural-language queries and long-form job descriptions.
  • Constraint Satisfaction: Enforce hard criteria (e.g., must be authorized to work in the US, minimum 4 years of experience) while performing soft semantic matching on technical skills and domain experience.
  • Noise & Normalization: Robustly normalize non-standard titles, infer missing attributes (e.g., estimating seniority from work history dates), and resolve skill synonyms.
  • Explainability: Provide clear match rationales (e.g., highlighted matching projects, key skills overlap) to assist recruiters in decision-making.

Non-Functional Requirements & Scale

  • Profile Corpus: 10 million candidate profiles, with ~50,000 updates or additions per day.
  • Query Traffic: 50 QPS peak search traffic.
  • Latency: P95 search response time under 300 ms for first-page results (top 20 candidates).
  • Data Freshness: Candidate updates should be searchable within 15 minutes.

What Your Answer Should Cover

  1. Data Ingestion & Profile Representation: Document parsing, entity extraction (skills, titles, education), embedding generation, and attribute imputation.
  2. Search Architecture: Multi-stage funnel (Retrieval -> Filtering -> First-stage Ranking -> Cross-Encoder / Second-stage Re-ranking).
  3. Hybrid Search Strategy: Combining sparse lexical retrieval (BM25 / Elasticsearch) with dense semantic vector search (ANN in Milvus/Pinecone/OpenSearch) and boolean filters.
  4. Model Training & Loss Objectives: How to train the representation and ranking models (contrastive learning, negative sampling strategies, click/interview feedback loops).
  5. Evaluation & Monitoring: Offline metrics (NDCG@K, Recall@K, MRR) and online metrics (interview acceptance rate, recruiter dwell time, search abandonment).

Hints

Hint 1

Consider decoupling hard filter enforcement from dense representation matching by utilizing metadata pre-filtering or hybrid inverted-index/vector databases.

Hint 2

For long-form job descriptions, standard bi-encoders face token length limits; explore chunk-level pooling, multi-vector representations, or extracting structured search criteria using an LLM prior to search.

Hint 3

Address cold-start candidate bias and position bias by incorporating exploration strategies and normalizing candidate visibility.

Follow-up questions

  • How would you handle negative recruiter feedback (e.g., a recruiter rejects multiple candidates from a specific background) without permanently degrading the candidate's global score?
  • If candidate profiles contain conflicting data between their raw resume text and their structured LinkedIn/self-reported profile, how does your system resolve this discrepancy?
  • How would you audit and mitigate demographic bias (gender, age, ethnicity) in your semantic embedding and ranking models?