Match Jobs for SEO Specialist Roles

A quick example on how you can match against listings with SEO Specialist profiles through our job feed data API with semantic search capabilities.

This basic example demonstrates how to use the vector embeddings and vector search capabilities of the jobdata API to match SEO Specialist job profiles based on semantic similarity. This way you can find job listings that align closely with a given job profile description, even if the exact keywords are not present in the job description. This is particularly useful for HR platforms, recruitment tools, and job boards aiming to improve candidate-job matching accuracy.

By leveraging our pre-generated embeddings and semantic search features, you can unlock powerful semantic matching capabilities to enhance your job search or recruitment platform. Whether you're matching candidates to jobs or analyzing job market trends, these features provide a robust solution for understanding and matching job profiles based on context and meaning.

Job Matching Example

The API provides pre-generated vector embeddings for all job posts, currently created using OpenAI's text-embedding-3-small model. These embeddings capture the semantic meaning of job titles and descriptions, enabling the API to perform semantic searches based on a job profile description.

When you provide a job profile description (e.g., "Skilled in optimizing websites for search engines, conducting keyword research, and improving organic search rankings."), the API:

Converts the text into a vector embedding in real-time.
Compares this embedding against the pre-generated embeddings of job posts using cosine similarity.
Returns job listings that are most semantically similar to the profile, ordered by relevance.

Here's a link to a live API results page (works without an API key for this example):
Skilled in optimizing websites for search engines, conducting keyword research, and improving organic search rankings.

The results include a cosine_dist field, which indicates the similarity between the query and each job post. Lower values represent higher semantic similarity.

Vector search is part of the access pro+ and access ultra plans, with a daily allowance of text-based queries. See the vector embeddings and search docs for all parameters.

Query the jobdata API

Example search request (with header auth):

curl -G -H "Authorization: Api-Key YOUR_API_KEY" \
  "https://jobdataapi.com/api/jobs/" \
  --data-urlencode "vec_text=Skilled in optimizing websites for search engines, conducting keyword research, and improving organic search rankings."

Example search request (with auth GET parameter):

curl -G "https://jobdataapi.com/api/jobs/" \
  -d api_key=YOUR_API_KEY \
  --data-urlencode "vec_text=Skilled in optimizing websites for search engines, conducting keyword research, and improving organic search rankings."

Shown below is a live example of what a list with the top matched jobs could look like, filtered by the job position profile from above with the most recent data from our global database:

Top matching jobs for this SEO Specialist profile