This article explores a practical AI use case grounded in insights from real-world implementations, conversations with B2B teams, and experience working with large-scale data.
It outlines how modern HR tech platforms can go beyond keyword-matching — using AI and structured web data to power high-precision candidate-job matching engines that understand not just what a job says, but who it’s really for.
What’s built?
An AI candidate matching system that pairs job seekers with job ads by deeply understanding three layers of context:
- The candidate’s profile: Skills, experience, company history, and role progression.
- The job ad: Required competencies, seniority expectations, and company stage.
- The team environment: Org structure and team makeup where the role sits.
Example: Two candidates both know Python — but one has led backend teams in VC-backed startups, while the other has focused on scripting in corporate IT. Same skill, very different fit.
Instead of matching resumes to job titles, this system identifies fit based on what successful hires have looked like in similar environments.
Who’s doing this already?
Platforms like Hirefly.ai and Endorsed.ai are leveraging AI to match candidates to roles more intelligently — going beyond titles to assess team fit, skill proximity, and job context. They’re great references for product thinking and positioning.
What data is used?
To understand fit with high fidelity, the system combines four structured data layers:
- Job Postings: Title, skills, department, seniority — for intent and demand.
- Candidate Profiles: Roles, inferred skills, tenure, trajectory — for experience and specialization.
- Company Metadata: Size, stage, industry, tech stack — to contextualize culture and pace.
- Team Signals: Role graph, reporting lines, team size — to evaluate team fit.
This layered approach enables a move from generic resumes to contextual candidate modeling — closer to how great recruiters think. Coresignal provides robust datasets across all four categories.
How to build a candidate matching system using Coresignal?
Here’s a practical, lean version of how to implement this system — optimized for flexibility and performance.
1. Extract and Normalize Key Features
Use Coresignal to pull:
- Candidate profiles: Title history, tenure, skills (explicit + inferred from job history)
- Job ads: Required skills, seniority, department, location
- Company data: Size, industry, funding stage, tech tags
- Org structure signals (if inferred): Peer roles, reporting chains
Clean the Data:
Normalize skills/titles using synonym mapping and embeddings (e.g., fastapi ≈ Python backend, SWE ≈ Software Engineer). Use open-source models like spaCy, Hugging Face, or OpenRefine.
2. Embed candidates, jobs, and context
- Convert cleaned candidate and job features into dense vector embeddings using models like SBERT, GTE, or OpenAI’s Embeddings API.
- Optionally, concatenate embeddings for team context (e.g., peer title mix, org depth).
Result: Each job and candidate is now represented as a multidimensional vector capturing skill, experience, and contextual nuance, not just keywords..
3. Push embeddings to a vector database
Store all embeddings in a high-performance vector database such as Pinecone, Weaviate, or Faiss.
This database becomes your search engine — where jobs and candidates can be retrieved based on contextual similarity, not just literal matching.
- Index all candidate vectors
- Support filtering by geography, remote eligibility, or industry experience
4. Match jobs to candidates and vice versa (contextual matching via approximate search)
To find top matches:
- Embed the incoming job posting or candidate query
- Use Approximate Nearest Neighbor (ANN) search to retrieve top matches
- Optional: Add re-ranking based on business logic (e.g., salary band, visa eligibility)
Use similarity metrics (cosine similarity or dot product) to score candidate-job fit.
5. Feedback loop for continuous learning
Matching models get better with feedback. Capture real-world outcomes to continuously refine embeddings and weights:
- Rejection reasons (e.g., “no team leadership”)
- Interview outcomes (e.g., failed technical screen)
- Hired candidates (positive samples)
Retrain monthly or quarterly depending on hiring volume. Use contrastive learning to refine embeddings with labeled matches/mismatches.
Example tech stack
- Data: Coresignal datasets or APIs (Jobs, Employee, Company)
- Embedding Model: SBERT, OpenAI, or GTE (Google)
- Vector Store: Pinecone, Weaviate, or Faiss
- Serving: FastAPI microservice
- UI: React frontend or ATS plugin
- Orchestration: Airflow for updates + refreshes
Scaling considerations
- Candidate cold starts: Use pre-computed archetypes from similar profiles to improve first-pass matching.
- Localization: Normalize skill/titles across languages and geos using multilingual embeddings.
Final thoughts
The hiring edge no longer comes from a bigger resume pool — it comes from understanding fit at the team and context level.
With public datasets like Coresignal and a modern AI architecture, HR tech platforms can now build systems that:
- Prioritize who will thrive, not just who qualifies
- Learn from every successful (and failed) hire
- Embed directly into workflows with explainability and speed
This is what modern recruiting looks like — data-first, context-aware, and continuously learning.
