Neural Search Jobs

curl --request POST \
  --url https://api.hirebase.org/v2/jobs/neural-search \
  --header 'Content-Type: application/json' \
  --data '
{
  "vector": {
    "job_ids": [
      "<string>"
    ],
    "artifact_id": "<string>",
    "query": "<string>",
    "vectors": [
      [
        123
      ]
    ],
    "score_threshold": 123
  },
  "lexical": {
    "job_titles": [
      "<string>"
    ],
    "keywords": [
      "<string>"
    ],
    "company_keywords": [
      "<string>"
    ],
    "job_slug": "<string>",
    "company_slug": [
      "<string>"
    ],
    "location_group": "<string>",
    "location_types": [
      "<string>"
    ],
    "geo_locations": [
      {
        "city": "<string>",
        "region": "<string>",
        "country": "<string>"
      }
    ],
    "experience": [
      "<string>"
    ],
    "yoe": {
      "min": 123,
      "max": 123
    },
    "include_yoe": "<string>",
    "company_types": [
      "<string>"
    ],
    "company_name": "<string>",
    "date_posted": "<string>",
    "days_ago": 123,
    "month": "<string>",
    "salary": {
      "min": 123,
      "max": 123
    },
    "include_no_salary": "<string>",
    "currency": "<string>",
    "job_types": [
      "<string>"
    ],
    "job_category": [
      "<string>"
    ],
    "industry": [
      "<string>"
    ],
    "sub_industry": [
      "<string>"
    ],
    "visa": "<string>",
    "filter_incomplete_jobs": "<string>",
    "include_expired": "<string>",
    "hide_seen_jobs": "<string>",
    "user_id": "<string>",
    "job_board": [
      "<string>"
    ],
    "sort_by": "<string>",
    "sort_order": "<string>",
    "page": 123,
    "limit": 123
  }
}
'

import requests

url = "https://api.hirebase.org/v2/jobs/neural-search"

payload = {
    "vector": {
        "job_ids": ["<string>"],
        "artifact_id": "<string>",
        "query": "<string>",
        "vectors": [[123]],
        "score_threshold": 123
    },
    "lexical": {
        "job_titles": ["<string>"],
        "keywords": ["<string>"],
        "company_keywords": ["<string>"],
        "job_slug": "<string>",
        "company_slug": ["<string>"],
        "location_group": "<string>",
        "location_types": ["<string>"],
        "geo_locations": [
            {
                "city": "<string>",
                "region": "<string>",
                "country": "<string>"
            }
        ],
        "experience": ["<string>"],
        "yoe": {
            "min": 123,
            "max": 123
        },
        "include_yoe": "<string>",
        "company_types": ["<string>"],
        "company_name": "<string>",
        "date_posted": "<string>",
        "days_ago": 123,
        "month": "<string>",
        "salary": {
            "min": 123,
            "max": 123
        },
        "include_no_salary": "<string>",
        "currency": "<string>",
        "job_types": ["<string>"],
        "job_category": ["<string>"],
        "industry": ["<string>"],
        "sub_industry": ["<string>"],
        "visa": "<string>",
        "filter_incomplete_jobs": "<string>",
        "include_expired": "<string>",
        "hide_seen_jobs": "<string>",
        "user_id": "<string>",
        "job_board": ["<string>"],
        "sort_by": "<string>",
        "sort_order": "<string>",
        "page": 123,
        "limit": 123
    }
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    vector: {
      job_ids: ['<string>'],
      artifact_id: '<string>',
      query: '<string>',
      vectors: [[123]],
      score_threshold: 123
    },
    lexical: {
      job_titles: ['<string>'],
      keywords: ['<string>'],
      company_keywords: ['<string>'],
      job_slug: '<string>',
      company_slug: ['<string>'],
      location_group: '<string>',
      location_types: ['<string>'],
      geo_locations: [{city: '<string>', region: '<string>', country: '<string>'}],
      experience: ['<string>'],
      yoe: {min: 123, max: 123},
      include_yoe: '<string>',
      company_types: ['<string>'],
      company_name: '<string>',
      date_posted: '<string>',
      days_ago: 123,
      month: '<string>',
      salary: {min: 123, max: 123},
      include_no_salary: '<string>',
      currency: '<string>',
      job_types: ['<string>'],
      job_category: ['<string>'],
      industry: ['<string>'],
      sub_industry: ['<string>'],
      visa: '<string>',
      filter_incomplete_jobs: '<string>',
      include_expired: '<string>',
      hide_seen_jobs: '<string>',
      user_id: '<string>',
      job_board: ['<string>'],
      sort_by: '<string>',
      sort_order: '<string>',
      page: 123,
      limit: 123
    }
  })
};

fetch('https://api.hirebase.org/v2/jobs/neural-search', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.hirebase.org/v2/jobs/neural-search",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'vector' => [
        'job_ids' => [
                '<string>'
        ],
        'artifact_id' => '<string>',
        'query' => '<string>',
        'vectors' => [
                [
                                123
                ]
        ],
        'score_threshold' => 123
    ],
    'lexical' => [
        'job_titles' => [
                '<string>'
        ],
        'keywords' => [
                '<string>'
        ],
        'company_keywords' => [
                '<string>'
        ],
        'job_slug' => '<string>',
        'company_slug' => [
                '<string>'
        ],
        'location_group' => '<string>',
        'location_types' => [
                '<string>'
        ],
        'geo_locations' => [
                [
                                'city' => '<string>',
                                'region' => '<string>',
                                'country' => '<string>'
                ]
        ],
        'experience' => [
                '<string>'
        ],
        'yoe' => [
                'min' => 123,
                'max' => 123
        ],
        'include_yoe' => '<string>',
        'company_types' => [
                '<string>'
        ],
        'company_name' => '<string>',
        'date_posted' => '<string>',
        'days_ago' => 123,
        'month' => '<string>',
        'salary' => [
                'min' => 123,
                'max' => 123
        ],
        'include_no_salary' => '<string>',
        'currency' => '<string>',
        'job_types' => [
                '<string>'
        ],
        'job_category' => [
                '<string>'
        ],
        'industry' => [
                '<string>'
        ],
        'sub_industry' => [
                '<string>'
        ],
        'visa' => '<string>',
        'filter_incomplete_jobs' => '<string>',
        'include_expired' => '<string>',
        'hide_seen_jobs' => '<string>',
        'user_id' => '<string>',
        'job_board' => [
                '<string>'
        ],
        'sort_by' => '<string>',
        'sort_order' => '<string>',
        'page' => 123,
        'limit' => 123
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.hirebase.org/v2/jobs/neural-search"

	payload := strings.NewReader("{\n  \"vector\": {\n    \"job_ids\": [\n      \"<string>\"\n    ],\n    \"artifact_id\": \"<string>\",\n    \"query\": \"<string>\",\n    \"vectors\": [\n      [\n        123\n      ]\n    ],\n    \"score_threshold\": 123\n  },\n  \"lexical\": {\n    \"job_titles\": [\n      \"<string>\"\n    ],\n    \"keywords\": [\n      \"<string>\"\n    ],\n    \"company_keywords\": [\n      \"<string>\"\n    ],\n    \"job_slug\": \"<string>\",\n    \"company_slug\": [\n      \"<string>\"\n    ],\n    \"location_group\": \"<string>\",\n    \"location_types\": [\n      \"<string>\"\n    ],\n    \"geo_locations\": [\n      {\n        \"city\": \"<string>\",\n        \"region\": \"<string>\",\n        \"country\": \"<string>\"\n      }\n    ],\n    \"experience\": [\n      \"<string>\"\n    ],\n    \"yoe\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_yoe\": \"<string>\",\n    \"company_types\": [\n      \"<string>\"\n    ],\n    \"company_name\": \"<string>\",\n    \"date_posted\": \"<string>\",\n    \"days_ago\": 123,\n    \"month\": \"<string>\",\n    \"salary\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_no_salary\": \"<string>\",\n    \"currency\": \"<string>\",\n    \"job_types\": [\n      \"<string>\"\n    ],\n    \"job_category\": [\n      \"<string>\"\n    ],\n    \"industry\": [\n      \"<string>\"\n    ],\n    \"sub_industry\": [\n      \"<string>\"\n    ],\n    \"visa\": \"<string>\",\n    \"filter_incomplete_jobs\": \"<string>\",\n    \"include_expired\": \"<string>\",\n    \"hide_seen_jobs\": \"<string>\",\n    \"user_id\": \"<string>\",\n    \"job_board\": [\n      \"<string>\"\n    ],\n    \"sort_by\": \"<string>\",\n    \"sort_order\": \"<string>\",\n    \"page\": 123,\n    \"limit\": 123\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.hirebase.org/v2/jobs/neural-search")
  .header("Content-Type", "application/json")
  .body("{\n  \"vector\": {\n    \"job_ids\": [\n      \"<string>\"\n    ],\n    \"artifact_id\": \"<string>\",\n    \"query\": \"<string>\",\n    \"vectors\": [\n      [\n        123\n      ]\n    ],\n    \"score_threshold\": 123\n  },\n  \"lexical\": {\n    \"job_titles\": [\n      \"<string>\"\n    ],\n    \"keywords\": [\n      \"<string>\"\n    ],\n    \"company_keywords\": [\n      \"<string>\"\n    ],\n    \"job_slug\": \"<string>\",\n    \"company_slug\": [\n      \"<string>\"\n    ],\n    \"location_group\": \"<string>\",\n    \"location_types\": [\n      \"<string>\"\n    ],\n    \"geo_locations\": [\n      {\n        \"city\": \"<string>\",\n        \"region\": \"<string>\",\n        \"country\": \"<string>\"\n      }\n    ],\n    \"experience\": [\n      \"<string>\"\n    ],\n    \"yoe\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_yoe\": \"<string>\",\n    \"company_types\": [\n      \"<string>\"\n    ],\n    \"company_name\": \"<string>\",\n    \"date_posted\": \"<string>\",\n    \"days_ago\": 123,\n    \"month\": \"<string>\",\n    \"salary\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_no_salary\": \"<string>\",\n    \"currency\": \"<string>\",\n    \"job_types\": [\n      \"<string>\"\n    ],\n    \"job_category\": [\n      \"<string>\"\n    ],\n    \"industry\": [\n      \"<string>\"\n    ],\n    \"sub_industry\": [\n      \"<string>\"\n    ],\n    \"visa\": \"<string>\",\n    \"filter_incomplete_jobs\": \"<string>\",\n    \"include_expired\": \"<string>\",\n    \"hide_seen_jobs\": \"<string>\",\n    \"user_id\": \"<string>\",\n    \"job_board\": [\n      \"<string>\"\n    ],\n    \"sort_by\": \"<string>\",\n    \"sort_order\": \"<string>\",\n    \"page\": 123,\n    \"limit\": 123\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.hirebase.org/v2/jobs/neural-search")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"vector\": {\n    \"job_ids\": [\n      \"<string>\"\n    ],\n    \"artifact_id\": \"<string>\",\n    \"query\": \"<string>\",\n    \"vectors\": [\n      [\n        123\n      ]\n    ],\n    \"score_threshold\": 123\n  },\n  \"lexical\": {\n    \"job_titles\": [\n      \"<string>\"\n    ],\n    \"keywords\": [\n      \"<string>\"\n    ],\n    \"company_keywords\": [\n      \"<string>\"\n    ],\n    \"job_slug\": \"<string>\",\n    \"company_slug\": [\n      \"<string>\"\n    ],\n    \"location_group\": \"<string>\",\n    \"location_types\": [\n      \"<string>\"\n    ],\n    \"geo_locations\": [\n      {\n        \"city\": \"<string>\",\n        \"region\": \"<string>\",\n        \"country\": \"<string>\"\n      }\n    ],\n    \"experience\": [\n      \"<string>\"\n    ],\n    \"yoe\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_yoe\": \"<string>\",\n    \"company_types\": [\n      \"<string>\"\n    ],\n    \"company_name\": \"<string>\",\n    \"date_posted\": \"<string>\",\n    \"days_ago\": 123,\n    \"month\": \"<string>\",\n    \"salary\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_no_salary\": \"<string>\",\n    \"currency\": \"<string>\",\n    \"job_types\": [\n      \"<string>\"\n    ],\n    \"job_category\": [\n      \"<string>\"\n    ],\n    \"industry\": [\n      \"<string>\"\n    ],\n    \"sub_industry\": [\n      \"<string>\"\n    ],\n    \"visa\": \"<string>\",\n    \"filter_incomplete_jobs\": \"<string>\",\n    \"include_expired\": \"<string>\",\n    \"hide_seen_jobs\": \"<string>\",\n    \"user_id\": \"<string>\",\n    \"job_board\": [\n      \"<string>\"\n    ],\n    \"sort_by\": \"<string>\",\n    \"sort_order\": \"<string>\",\n    \"page\": 123,\n    \"limit\": 123\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "jobs": [
    {
      "_id": "<string>",
      "job_title": "<string>",
      "job_title_raw": "<string>",
      "description": "<string>",
      "application_link": "<string>",
      "job_categories": [
        {}
      ],
      "job_type": "<string>",
      "location_type": "<string>",
      "location_raw": "<string>",
      "locations": [
        {
          "city": "<string>",
          "region": "<string>",
          "country": "<string>"
        }
      ],
      "salary_range": {
        "min": 123,
        "max": 123,
        "currency": "<string>",
        "period": "<string>"
      },
      "yoe_range": {
        "min": 123,
        "max": 123
      },
      "experience_level": {},
      "education_level": "<string>",
      "skills": [
        "<string>"
      ],
      "technologies": [
        "<string>"
      ],
      "benefits": [
        "<string>"
      ],
      "requirements_summary": "<string>",
      "team": "<string>",
      "language": "<string>",
      "visa_sponsored": true,
      "recruiter_agency": true,
      "offers_equity": true,
      "date_posted": "<string>",
      "job_board": "<string>",
      "job_board_link": "<string>",
      "job_slug": "<string>",
      "company_name": "<string>",
      "company_slug": "<string>",
      "company_link": "<string>",
      "company_logo": "<string>",
      "md5_hash": "<string>",
      "platform_job_id": "<string>",
      "contact_email": {},
      "contact_phone": {},
      "coolness_score": 123,
      "flexibility_score": 123,
      "compensation_value_score": 123,
      "benefits_score": 123,
      "impact_autonomy_score": 123,
      "prestige_score": 123,
      "growth_score": 123,
      "meta_completeness": true,
      "meta_targetability": 123,
      "company_data": {
        "description_summary": "<string>",
        "linkedin_link": {},
        "services": [
          "<string>"
        ],
        "size_range": {
          "min": 123,
          "max": 123
        },
        "industries": [
          {}
        ],
        "subindustries": [
          {}
        ],
        "type": {},
        "is_recruiting_agency": {},
        "is_3rd_party_agency": {}
      }
    }
  ],
  "total_count": 123,
  "page": 123,
  "limit": 123,
  "total_pages": 123
}

POST

jobs

neural-search

Neural Search Jobs

curl --request POST \
  --url https://api.hirebase.org/v2/jobs/neural-search \
  --header 'Content-Type: application/json' \
  --data '
{
  "vector": {
    "job_ids": [
      "<string>"
    ],
    "artifact_id": "<string>",
    "query": "<string>",
    "vectors": [
      [
        123
      ]
    ],
    "score_threshold": 123
  },
  "lexical": {
    "job_titles": [
      "<string>"
    ],
    "keywords": [
      "<string>"
    ],
    "company_keywords": [
      "<string>"
    ],
    "job_slug": "<string>",
    "company_slug": [
      "<string>"
    ],
    "location_group": "<string>",
    "location_types": [
      "<string>"
    ],
    "geo_locations": [
      {
        "city": "<string>",
        "region": "<string>",
        "country": "<string>"
      }
    ],
    "experience": [
      "<string>"
    ],
    "yoe": {
      "min": 123,
      "max": 123
    },
    "include_yoe": "<string>",
    "company_types": [
      "<string>"
    ],
    "company_name": "<string>",
    "date_posted": "<string>",
    "days_ago": 123,
    "month": "<string>",
    "salary": {
      "min": 123,
      "max": 123
    },
    "include_no_salary": "<string>",
    "currency": "<string>",
    "job_types": [
      "<string>"
    ],
    "job_category": [
      "<string>"
    ],
    "industry": [
      "<string>"
    ],
    "sub_industry": [
      "<string>"
    ],
    "visa": "<string>",
    "filter_incomplete_jobs": "<string>",
    "include_expired": "<string>",
    "hide_seen_jobs": "<string>",
    "user_id": "<string>",
    "job_board": [
      "<string>"
    ],
    "sort_by": "<string>",
    "sort_order": "<string>",
    "page": 123,
    "limit": 123
  }
}
'

import requests

url = "https://api.hirebase.org/v2/jobs/neural-search"

payload = {
    "vector": {
        "job_ids": ["<string>"],
        "artifact_id": "<string>",
        "query": "<string>",
        "vectors": [[123]],
        "score_threshold": 123
    },
    "lexical": {
        "job_titles": ["<string>"],
        "keywords": ["<string>"],
        "company_keywords": ["<string>"],
        "job_slug": "<string>",
        "company_slug": ["<string>"],
        "location_group": "<string>",
        "location_types": ["<string>"],
        "geo_locations": [
            {
                "city": "<string>",
                "region": "<string>",
                "country": "<string>"
            }
        ],
        "experience": ["<string>"],
        "yoe": {
            "min": 123,
            "max": 123
        },
        "include_yoe": "<string>",
        "company_types": ["<string>"],
        "company_name": "<string>",
        "date_posted": "<string>",
        "days_ago": 123,
        "month": "<string>",
        "salary": {
            "min": 123,
            "max": 123
        },
        "include_no_salary": "<string>",
        "currency": "<string>",
        "job_types": ["<string>"],
        "job_category": ["<string>"],
        "industry": ["<string>"],
        "sub_industry": ["<string>"],
        "visa": "<string>",
        "filter_incomplete_jobs": "<string>",
        "include_expired": "<string>",
        "hide_seen_jobs": "<string>",
        "user_id": "<string>",
        "job_board": ["<string>"],
        "sort_by": "<string>",
        "sort_order": "<string>",
        "page": 123,
        "limit": 123
    }
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    vector: {
      job_ids: ['<string>'],
      artifact_id: '<string>',
      query: '<string>',
      vectors: [[123]],
      score_threshold: 123
    },
    lexical: {
      job_titles: ['<string>'],
      keywords: ['<string>'],
      company_keywords: ['<string>'],
      job_slug: '<string>',
      company_slug: ['<string>'],
      location_group: '<string>',
      location_types: ['<string>'],
      geo_locations: [{city: '<string>', region: '<string>', country: '<string>'}],
      experience: ['<string>'],
      yoe: {min: 123, max: 123},
      include_yoe: '<string>',
      company_types: ['<string>'],
      company_name: '<string>',
      date_posted: '<string>',
      days_ago: 123,
      month: '<string>',
      salary: {min: 123, max: 123},
      include_no_salary: '<string>',
      currency: '<string>',
      job_types: ['<string>'],
      job_category: ['<string>'],
      industry: ['<string>'],
      sub_industry: ['<string>'],
      visa: '<string>',
      filter_incomplete_jobs: '<string>',
      include_expired: '<string>',
      hide_seen_jobs: '<string>',
      user_id: '<string>',
      job_board: ['<string>'],
      sort_by: '<string>',
      sort_order: '<string>',
      page: 123,
      limit: 123
    }
  })
};

fetch('https://api.hirebase.org/v2/jobs/neural-search', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.hirebase.org/v2/jobs/neural-search",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'vector' => [
        'job_ids' => [
                '<string>'
        ],
        'artifact_id' => '<string>',
        'query' => '<string>',
        'vectors' => [
                [
                                123
                ]
        ],
        'score_threshold' => 123
    ],
    'lexical' => [
        'job_titles' => [
                '<string>'
        ],
        'keywords' => [
                '<string>'
        ],
        'company_keywords' => [
                '<string>'
        ],
        'job_slug' => '<string>',
        'company_slug' => [
                '<string>'
        ],
        'location_group' => '<string>',
        'location_types' => [
                '<string>'
        ],
        'geo_locations' => [
                [
                                'city' => '<string>',
                                'region' => '<string>',
                                'country' => '<string>'
                ]
        ],
        'experience' => [
                '<string>'
        ],
        'yoe' => [
                'min' => 123,
                'max' => 123
        ],
        'include_yoe' => '<string>',
        'company_types' => [
                '<string>'
        ],
        'company_name' => '<string>',
        'date_posted' => '<string>',
        'days_ago' => 123,
        'month' => '<string>',
        'salary' => [
                'min' => 123,
                'max' => 123
        ],
        'include_no_salary' => '<string>',
        'currency' => '<string>',
        'job_types' => [
                '<string>'
        ],
        'job_category' => [
                '<string>'
        ],
        'industry' => [
                '<string>'
        ],
        'sub_industry' => [
                '<string>'
        ],
        'visa' => '<string>',
        'filter_incomplete_jobs' => '<string>',
        'include_expired' => '<string>',
        'hide_seen_jobs' => '<string>',
        'user_id' => '<string>',
        'job_board' => [
                '<string>'
        ],
        'sort_by' => '<string>',
        'sort_order' => '<string>',
        'page' => 123,
        'limit' => 123
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.hirebase.org/v2/jobs/neural-search"

	payload := strings.NewReader("{\n  \"vector\": {\n    \"job_ids\": [\n      \"<string>\"\n    ],\n    \"artifact_id\": \"<string>\",\n    \"query\": \"<string>\",\n    \"vectors\": [\n      [\n        123\n      ]\n    ],\n    \"score_threshold\": 123\n  },\n  \"lexical\": {\n    \"job_titles\": [\n      \"<string>\"\n    ],\n    \"keywords\": [\n      \"<string>\"\n    ],\n    \"company_keywords\": [\n      \"<string>\"\n    ],\n    \"job_slug\": \"<string>\",\n    \"company_slug\": [\n      \"<string>\"\n    ],\n    \"location_group\": \"<string>\",\n    \"location_types\": [\n      \"<string>\"\n    ],\n    \"geo_locations\": [\n      {\n        \"city\": \"<string>\",\n        \"region\": \"<string>\",\n        \"country\": \"<string>\"\n      }\n    ],\n    \"experience\": [\n      \"<string>\"\n    ],\n    \"yoe\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_yoe\": \"<string>\",\n    \"company_types\": [\n      \"<string>\"\n    ],\n    \"company_name\": \"<string>\",\n    \"date_posted\": \"<string>\",\n    \"days_ago\": 123,\n    \"month\": \"<string>\",\n    \"salary\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_no_salary\": \"<string>\",\n    \"currency\": \"<string>\",\n    \"job_types\": [\n      \"<string>\"\n    ],\n    \"job_category\": [\n      \"<string>\"\n    ],\n    \"industry\": [\n      \"<string>\"\n    ],\n    \"sub_industry\": [\n      \"<string>\"\n    ],\n    \"visa\": \"<string>\",\n    \"filter_incomplete_jobs\": \"<string>\",\n    \"include_expired\": \"<string>\",\n    \"hide_seen_jobs\": \"<string>\",\n    \"user_id\": \"<string>\",\n    \"job_board\": [\n      \"<string>\"\n    ],\n    \"sort_by\": \"<string>\",\n    \"sort_order\": \"<string>\",\n    \"page\": 123,\n    \"limit\": 123\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.hirebase.org/v2/jobs/neural-search")
  .header("Content-Type", "application/json")
  .body("{\n  \"vector\": {\n    \"job_ids\": [\n      \"<string>\"\n    ],\n    \"artifact_id\": \"<string>\",\n    \"query\": \"<string>\",\n    \"vectors\": [\n      [\n        123\n      ]\n    ],\n    \"score_threshold\": 123\n  },\n  \"lexical\": {\n    \"job_titles\": [\n      \"<string>\"\n    ],\n    \"keywords\": [\n      \"<string>\"\n    ],\n    \"company_keywords\": [\n      \"<string>\"\n    ],\n    \"job_slug\": \"<string>\",\n    \"company_slug\": [\n      \"<string>\"\n    ],\n    \"location_group\": \"<string>\",\n    \"location_types\": [\n      \"<string>\"\n    ],\n    \"geo_locations\": [\n      {\n        \"city\": \"<string>\",\n        \"region\": \"<string>\",\n        \"country\": \"<string>\"\n      }\n    ],\n    \"experience\": [\n      \"<string>\"\n    ],\n    \"yoe\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_yoe\": \"<string>\",\n    \"company_types\": [\n      \"<string>\"\n    ],\n    \"company_name\": \"<string>\",\n    \"date_posted\": \"<string>\",\n    \"days_ago\": 123,\n    \"month\": \"<string>\",\n    \"salary\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_no_salary\": \"<string>\",\n    \"currency\": \"<string>\",\n    \"job_types\": [\n      \"<string>\"\n    ],\n    \"job_category\": [\n      \"<string>\"\n    ],\n    \"industry\": [\n      \"<string>\"\n    ],\n    \"sub_industry\": [\n      \"<string>\"\n    ],\n    \"visa\": \"<string>\",\n    \"filter_incomplete_jobs\": \"<string>\",\n    \"include_expired\": \"<string>\",\n    \"hide_seen_jobs\": \"<string>\",\n    \"user_id\": \"<string>\",\n    \"job_board\": [\n      \"<string>\"\n    ],\n    \"sort_by\": \"<string>\",\n    \"sort_order\": \"<string>\",\n    \"page\": 123,\n    \"limit\": 123\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.hirebase.org/v2/jobs/neural-search")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"vector\": {\n    \"job_ids\": [\n      \"<string>\"\n    ],\n    \"artifact_id\": \"<string>\",\n    \"query\": \"<string>\",\n    \"vectors\": [\n      [\n        123\n      ]\n    ],\n    \"score_threshold\": 123\n  },\n  \"lexical\": {\n    \"job_titles\": [\n      \"<string>\"\n    ],\n    \"keywords\": [\n      \"<string>\"\n    ],\n    \"company_keywords\": [\n      \"<string>\"\n    ],\n    \"job_slug\": \"<string>\",\n    \"company_slug\": [\n      \"<string>\"\n    ],\n    \"location_group\": \"<string>\",\n    \"location_types\": [\n      \"<string>\"\n    ],\n    \"geo_locations\": [\n      {\n        \"city\": \"<string>\",\n        \"region\": \"<string>\",\n        \"country\": \"<string>\"\n      }\n    ],\n    \"experience\": [\n      \"<string>\"\n    ],\n    \"yoe\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_yoe\": \"<string>\",\n    \"company_types\": [\n      \"<string>\"\n    ],\n    \"company_name\": \"<string>\",\n    \"date_posted\": \"<string>\",\n    \"days_ago\": 123,\n    \"month\": \"<string>\",\n    \"salary\": {\n      \"min\": 123,\n      \"max\": 123\n    },\n    \"include_no_salary\": \"<string>\",\n    \"currency\": \"<string>\",\n    \"job_types\": [\n      \"<string>\"\n    ],\n    \"job_category\": [\n      \"<string>\"\n    ],\n    \"industry\": [\n      \"<string>\"\n    ],\n    \"sub_industry\": [\n      \"<string>\"\n    ],\n    \"visa\": \"<string>\",\n    \"filter_incomplete_jobs\": \"<string>\",\n    \"include_expired\": \"<string>\",\n    \"hide_seen_jobs\": \"<string>\",\n    \"user_id\": \"<string>\",\n    \"job_board\": [\n      \"<string>\"\n    ],\n    \"sort_by\": \"<string>\",\n    \"sort_order\": \"<string>\",\n    \"page\": 123,\n    \"limit\": 123\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "jobs": [
    {
      "_id": "<string>",
      "job_title": "<string>",
      "job_title_raw": "<string>",
      "description": "<string>",
      "application_link": "<string>",
      "job_categories": [
        {}
      ],
      "job_type": "<string>",
      "location_type": "<string>",
      "location_raw": "<string>",
      "locations": [
        {
          "city": "<string>",
          "region": "<string>",
          "country": "<string>"
        }
      ],
      "salary_range": {
        "min": 123,
        "max": 123,
        "currency": "<string>",
        "period": "<string>"
      },
      "yoe_range": {
        "min": 123,
        "max": 123
      },
      "experience_level": {},
      "education_level": "<string>",
      "skills": [
        "<string>"
      ],
      "technologies": [
        "<string>"
      ],
      "benefits": [
        "<string>"
      ],
      "requirements_summary": "<string>",
      "team": "<string>",
      "language": "<string>",
      "visa_sponsored": true,
      "recruiter_agency": true,
      "offers_equity": true,
      "date_posted": "<string>",
      "job_board": "<string>",
      "job_board_link": "<string>",
      "job_slug": "<string>",
      "company_name": "<string>",
      "company_slug": "<string>",
      "company_link": "<string>",
      "company_logo": "<string>",
      "md5_hash": "<string>",
      "platform_job_id": "<string>",
      "contact_email": {},
      "contact_phone": {},
      "coolness_score": 123,
      "flexibility_score": 123,
      "compensation_value_score": 123,
      "benefits_score": 123,
      "impact_autonomy_score": 123,
      "prestige_score": 123,
      "growth_score": 123,
      "meta_completeness": true,
      "meta_targetability": 123,
      "company_data": {
        "description_summary": "<string>",
        "linkedin_link": {},
        "services": [
          "<string>"
        ],
        "size_range": {
          "min": 123,
          "max": 123
        },
        "industries": [
          {}
        ],
        "subindustries": [
          {}
        ],
        "type": {},
        "is_recruiting_agency": {},
        "is_3rd_party_agency": {}
      }
    }
  ],
  "total_count": 123,
  "page": 123,
  "limit": 123,
  "total_pages": 123
}

This endpoint enables you to perform hybrid searches: apply familiar filters (titles, locations, salary, etc.) while simultaneously ranking results by vector similarity to a text query or embedding.

Endpoint

POST /v2/jobs/neural-search

Request Body

Note:

You can supply only vector, only lexical, or both; at least one must be present.
If you include both, results are ordered by combined ranking (semantic score then lexical rank).
Unknown top-level fields are rejected with 422. Unknown fields inside vector and lexical (for example lexical.job_type instead of job_types) are ignored, so a misspelled filter returns unfiltered results. Check field names against this page.

object

Parameters for semantic, embedding-based search

Show child attributes

string[]

“More like these”: the embeddings of the listed jobs are used as the query, so results are the jobs most similar to them. Pass the _id of the job a user is viewing to power a “Similar jobs” rail.

string

ID of the embedding artifact (e.g., resume) to use. Use the _id returned by Upload Resume.

string

Semantic text query to be encoded into a vector for similarity matching.

number[][]

Explicit embedding vectors (each must be 768 elements long) to compare against job vectors.

number

Minimum similarity score (0.0–1.0) for a job to appear in jobs. Defaults to 0.0. Note: filters the returned jobs only — total_count still reflects the endpoint cap.

object

Traditional filtering parameters

Show child attributes

string[]

Array of job titles to search for (e.g., ["Software Engineer", "Data Scientist"]).

string[]

Array of keywords to match within job descriptions.

string[]

Array of keywords to match in company descriptions, services, and products.

string

Filter by a specific job’s slug (deep-link identifier).

string[]

Filter by one company slug or a list of slugs (single string or array).

string

Predefined location set (e.g., "Bay_Area"). Each group covers a 25-mile radius around each of its cities; an unknown group name returns 422.

string[]

Work arrangements to include. Accepted values: "Remote", "Hybrid", "In-Person".

object[]

Array of geographic filters.

Show child attributes

string

City name

string

State, region or province

string

Country name

string[]

Experience level categories. Accepted values: "Entry", "Junior", "Mid", "Senior", "Executive".

object

Years-of-experience range.

Show child attributes

number

Minimum years

number

Maximum years

string

Set to "true" to include jobs without specified years of experience.

string[]

Filter jobs by the hiring company’s headcount bucket. Accepted values: "1-10", "11-50", "51-200", "201-500", "501-1000", "1001-5000", "5001-10000", "10000+". Pass multiple values to combine ranges (e.g., ["11-50", "51-200"]).

This parameter is named company_types for historical reasons but currently filters by company size. A separate classification filter (Public Company / Non-Profit / etc.) will be added in a future API update.

string

Exact company name filter.

string

Relative timeframe (e.g., "today", "last_week", "last_month").

number

Jobs posted in the last n days (e.g., 7).

string

Specific month in YYYY-MM format (e.g., "2023-12").

⚠️ Not currently applied — this filter is accepted but does not affect results yet. Use days_ago or date_posted to filter by posting time.

object

Salary range filter.

Show child attributes

number

Minimum salary

number

Maximum salary

string

Set to "true" to include listings without salary data.

string

Three-letter currency code (e.g., "USD").

string[]

Array of employment types. Accepted values: "Full Time", "Part Time", "Contract", "Internship".

The "Contract" value matches roles whose returned job_type is "Contract / Temporary" — request token and response label differ.

The correct field name is job_types (plural). job_type (singular) is an unknown field and is ignored, so it does not filter results.

string[]

Specific job categories (e.g., ["Data Science"]).

string[]

Industry to filter by (e.g., "Tech, Software & IT Services", "Healthcare"). Can be a single string or an array of strings. See List Industries for accepted values.

string[]

More specific industry categories.

string

Set to "true" to only include visa-sponsored roles.

string

Filter out jobs with incomplete listing data ("true" or "false").

string

Set to "true" to include expired listings.

string

Set to "true" to omit jobs the user has already viewed.

string

User’s MongoDB ObjectId for personalized filtering.

string[]

Source job boards (e.g., ["iCIMS","Greenhouse"]).

string

Chooses which lexical matches are ranked when more than 2,500 jobs match the filters (e.g. "date_posted" ranks the 2,500 most recent). Results are always returned in semantic-similarity order. Accepted values: "relevance", "date_posted", "salary", "yoe"; anything else falls back to "relevance".

string

"asc" or "desc".

number

default:"1"

Page number. The ranking for a query is computed on the first page and reused for 15 minutes, so later pages of the same query are fast and stay consistent with page 1.

number

default:"10"

Results per page

Response

array

Array of job objects matching the query.

Show child attributes

string

Unique job identifier

string

Job title

string

Unparsed job title as scraped from the source.

string

Full job description (HTML or text)

string

URL to apply

array

Tags/categories for the role

string

Employment type. One of: "Full Time", "Part Time", "Contract / Temporary", "Internship".

string

Work arrangement

string

Raw location string from the source job board (before normalization into locations).

array

Array of location objects

Show child attributes

string

City name

string

State or province

string

Country

object|null

Offered salary range

Show child attributes

number

Minimum salary

number

Maximum salary

string

Currency code

string

Pay period for the salary range. Observed values: "yearly", "monthly", "daily", "hourly".

This field is not fully normalized today — you may also see "year" (≡ "yearly") and "hour" (≡ "hourly"). Treat those as equivalent until normalized.

object

Required years of experience

Show child attributes

number

Min years

number

Max years

string | null

Parsed experience tier for the role (e.g., "Senior"). May be null when not inferred.

string

Required education level. One of: "No Education Required", "High school degree", "Associate degree", "Bachelor's degree", "Master's degree", "Doctoral degree".

string[]

List of skills mentioned in the job description

string[]

List of technologies mentioned in the job description

string[]

List of benefits mentioned in the job description

string

Brief qualifications summary

string

Team at the company hiring for the position

string

Language the job posting is in

boolean

Whether visa sponsorship is offered

boolean

Indicates whether the listing was posted by a recruiting agency

boolean

Whether the listing mentions equity compensation.

string

Posting date (format: YYYY-MM-DD).

string

Source job board

string

URL of the job board

string

Deep-link slug for the job

string

Name of hiring company

string

Company slug

string

Company website

string

URL to company logo

string

Content hash of the listing.

string

The source ATS’s own job identifier.

string | null

Contact email for the listing, when available. Paid users only.

string | null

Contact phone number for the listing, when available. Paid users only.

Dimension scores — coolness_score, flexibility_score, compensation_value_score, benefits_score, impact_autonomy_score, prestige_score, and growth_score each use a 0–10 scale (higher is better).

number

Company/role “coolness” rating (0–10).

number

Work-flexibility rating (0–10).

number

Compensation competitiveness rating (0–10).

number

Benefits package rating (0–10).

number

Role-level impact and autonomy rating (0–10).

number

Company/role prestige rating (0–10).

number

Career growth / learning opportunity rating (0–10).

boolean

Whether the parsed listing metadata is considered complete.

number

Internal signal (0–1) reflecting how targetable/complete the listing is for matching.

object

Enriched company profile

Show child attributes

string

Short company description

string|null

LinkedIn URL

string[]

List of services that the company provides

object|null

Employee count range

Show child attributes

number

Min headcount

number

Max headcount

array

Industry sectors

array

Detailed industry categories

string | null

Categorical company type (e.g., "Startup", "Enterprise").

boolean | null

Whether the company is a recruiting/talent agency.

boolean | null

Whether the company is a third-party agency.

number

Total number of matching jobs. Capped at 2,500 for this endpoint.

number

Current page number

number

Number of results returned

number

Total pages available

Rate limit: 100 requests per 60 seconds per API key. 429 responses include a Retry-After header. See Error Handling.Note: Results are ranked by semantic similarity internally, but a per-job vector_score field is not currently returned in the response.

Example Request

curl -X POST https://api.hirebase.org/v2/jobs/neural-search \
  -H "Content-Type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -d '{
    "vector": {
      "query": "senior backend engineer building distributed systems with Python and Kubernetes"
    },
    "lexical": {
      "location_types": ["Remote", "Hybrid"],
      "experience": ["Senior"],
      "industry": "Tech, Software & IT Services",
      "days_ago": 30,
      "limit": 10
    }
  }'

{
  "vector": {
    "query": "machine learning engineer"
  },
  "lexical": {
    "location_group": "Bay_Area",
    "experience": ["Mid", "Senior"],
    "yoe": {"min": 2, "max": 5},
    "job_types": ["Full Time"],
    "industry": "Tech, Software & IT Services",
    "page": 1,
    "limit": 5
  }
}

Example Response

{
  "jobs": [
    {
      "_id": "6a2f14a48bea0df96e26ac7b",
      "company_name": "CompanyXYZ",
      "job_title": "Senior Python Engineer",
      "job_title_raw": "Senior Python Engineer - Remote",
      "description": "<p>… cleaned/summarized description …</p>",
      "application_link": "https://jobs.workable.com/view/nqSdYtJwQ/...",
      "job_categories": ["Software Engineer Jobs", "Engineering Jobs", "Information Technology Jobs"],
      "job_type": "Full Time",
      "location_type": "Remote",
      "location_raw": "TELECOMMUTE; British Columbia, Canada",
      "locations": [
        {
          "city": null,
          "region": null,
          "country": "Canada",
          "coordinates": {"type": "Point", "coordinates": [-123.12, 49.28]},
          "bbox": [-123.2, 49.0, -122.9, 49.3],
          "address": "British Columbia, Canada"
        }
      ],
      "salary_range": {"min": 150000, "max": 210000, "currency": "USD", "period": "yearly"},
      "yoe_range": {"min": 2, "max": 5},
      "experience_level": "Senior",
      "education_level": "Bachelor's degree",
      "skills": ["Python", "FastAPI", "Distributed systems"],
      "technologies": ["Python", "PostgreSQL", "AWS"],
      "benefits": ["401k matching", "Remote-first", "Equity"],
      "requirements_summary": "5+ years Python; distributed systems experience",
      "team": "Platform Engineering",
      "language": "en",
      "visa_sponsored": false,
      "recruiter_agency": false,
      "offers_equity": false,
      "date_posted": "2026-06-07",
      "job_board": "workable",
      "job_board_link": "https://jobs.workable.com/company/...",
      "job_slug": "senior-python-engineer-17",
      "company_slug": "companyxyz",
      "company_link": "companyxyz.com",
      "company_logo": "https://logos.hirebase.org/.../companyxyz-320x320-q95.jpg",
      "md5_hash": "d6b5d4e7b99d23b082b46e49c22c168f",
      "platform_job_id": "ad88b6ba-a905-4850-ad79-73beb0e97766",
      "coolness_score": 6.4,
      "flexibility_score": 6.1,
      "compensation_value_score": 6.0,
      "benefits_score": 5.0,
      "impact_autonomy_score": 7.1,
      "prestige_score": 5.2,
      "growth_score": 6.2,
      "meta_completeness": false,
      "meta_targetability": 0.8,
      "company_data": {
        "description_summary": "CompanyXYZ builds …",
        "linkedin_link": "https://www.linkedin.com/company/companyxyz",
        "size_range": {"min": 51, "max": 200},
        "industries": ["Tech, Software & IT Services"],
        "subindustries": ["AI & ML"],
        "services": ["Platform", "APIs"],
        "type": "Startup",
        "is_recruiting_agency": false,
        "is_3rd_party_agency": false
      }
    }
  ],
  "total_count": 23,
  "page": 1,
  "limit": 5,
  "total_pages": 5
}

Error Responses

422 Unprocessable Entity

Returned when the request is malformed, contains invalid parameter values, or contains unknown top-level fields.

500 Internal Server Error

Returned when an unexpected server-side error occurs.

403 Forbidden

Returned if you do not have access to this feature.

Jobs API

Expired & Historical Jobs API

Hiring Manager API

Companies API

Insights API

Salary Benchmark API

Reference Data API

Resumes

Tasks API

Billing & Usage

Error Handling

Neural Search Jobs

Endpoint

Request Body

Response

Example Request

Example Response

Error Responses

​Endpoint

​Request Body

​Response

​Example Request

​Example Response

​Error Responses

Endpoint

Request Body

Response

Example Request

Example Response

Error Responses