Merge branch 'main' into main

README.md

@@ -67,6 +67,7 @@ Optional
 ├── job_type (enum): fulltime, parttime, internship, contract
 ├── proxy (str): in format 'http://user:pass@host:port' or [https, socks]
 ├── is_remote (bool)
+├── full_description (bool): fetches full description for Indeed / LinkedIn (much slower)
 ├── results_wanted (int): number of job results to retrieve for each site specified in 'site_type'
 ├── easy_apply (bool): filters for jobs that are hosted on LinkedIn
 ├── country_indeed (enum): filters the country on Indeed (see below for correct spelling)

examples/JobSpy_AllSites.py

@@ -2,12 +2,11 @@ from jobspy import scrape_jobs
 import pandas as pd
 
 jobs: pd.DataFrame = scrape_jobs(
-    site_name=["indeed", "linkedin", "zip_recruiter"],
+    site_name=["indeed", "linkedin", "zip_recruiter", "glassdoor"],
     search_term="software engineer",
     location="Dallas, TX",
-    results_wanted=50,  # be wary the higher it is, the more likey you'll get blocked (rotating proxy should work tho)
+    results_wanted=25,  # be wary the higher it is, the more likey you'll get blocked (rotating proxy can help tho)
     country_indeed="USA",
-    offset=25  # start jobs from an offset (use if search failed and want to continue)
     # proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
 )
 

examples/JobSpy_LongScrape.py

@@ -0,0 +1,77 @@
+from jobspy import scrape_jobs
+import pandas as pd
+import os
+import time
+
+# creates csv a new filename if the jobs.csv already exists.
+csv_filename = "jobs.csv"
+counter = 1
+while os.path.exists(csv_filename):
+    csv_filename = f"jobs_{counter}.csv"
+    counter += 1
+
+# results wanted and offset
+results_wanted = 1000
+offset = 0
+
+all_jobs = []
+
+# max retries
+max_retries = 3
+
+# nuumber of results at each iteration
+results_in_each_iteration = 30
+
+while len(all_jobs) < results_wanted:
+    retry_count = 0
+    while retry_count < max_retries:
+        print("Doing from", offset, "to", offset + results_in_each_iteration, "jobs")
+        try:
+            jobs = scrape_jobs(
+                site_name=["indeed"],
+                search_term="software engineer",
+                # New York, NY
+                # Dallas, TX
+
+                # Los Angeles, CA
+                location="Los Angeles, CA",
+                results_wanted=min(results_in_each_iteration, results_wanted - len(all_jobs)),
+                country_indeed="USA",
+                offset=offset,
+                # proxy="http://jobspy:5a4vpWtj8EeJ2hoYzk@ca.smartproxy.com:20001",
+            )
+
+            # Add the scraped jobs to the list
+            all_jobs.extend(jobs.to_dict('records'))
+
+            # Increment the offset for the next page of results
+            offset += results_in_each_iteration
+
+            # Add a delay to avoid rate limiting (you can adjust the delay time as needed)
+            print(f"Scraped {len(all_jobs)} jobs")
+            print("Sleeping secs", 100 * (retry_count + 1))
+            time.sleep(100 * (retry_count + 1))  # Sleep for 2 seconds between requests
+
+            break  # Break out of the retry loop if successful
+        except Exception as e:
+            print(f"Error: {e}")
+            retry_count += 1
+            print("Sleeping secs before retry", 100 * (retry_count + 1))
+            time.sleep(100 * (retry_count + 1))
+            if retry_count >= max_retries:
+                print("Max retries reached. Exiting.")
+                break
+
+# DataFrame from the collected job data
+jobs_df = pd.DataFrame(all_jobs)
+
+# Formatting
+pd.set_option("display.max_columns", None)
+pd.set_option("display.max_rows", None)
+pd.set_option("display.width", None)
+pd.set_option("display.max_colwidth", 50)
+
+print(jobs_df)
+
+jobs_df.to_csv(csv_filename, index=False)
+print(f"Outputted to {csv_filename}")

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "python-jobspy"
-version = "1.1.31"
+version = "1.1.36"
 description = "Job scraper for LinkedIn, Indeed, Glassdoor & ZipRecruiter"
 authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
 homepage = "https://github.com/Bunsly/JobSpy"

src/jobspy/__init__.py

@@ -40,6 +40,7 @@ def scrape_jobs(
     country_indeed: str = "usa",
     hyperlinks: bool = False,
     proxy: Optional[str] = None,
+    full_description: Optional[bool] = False,
     offset: Optional[int] = 0,
 ) -> pd.DataFrame:
     """
@@ -74,6 +75,7 @@ def scrape_jobs(
         is_remote=is_remote,
         job_type=job_type,
         easy_apply=easy_apply,
+        full_description=full_description,
         results_wanted=results_wanted,
         offset=offset,
     )

src/jobspy/jobs/__init__.py

@@ -1,7 +1,7 @@
-from typing import Union, Optional
+from typing import Optional
 from datetime import date
 from enum import Enum
-from pydantic import BaseModel, validator
+from pydantic import BaseModel
 
 
 class JobType(Enum):

src/jobspy/scrapers/__init__.py

@@ -19,6 +19,7 @@ class ScraperInput(BaseModel):
     is_remote: bool = False
     job_type: Optional[JobType] = None
     easy_apply: bool = None  # linkedin
+    full_description: bool = False
     offset: int = 0
 
     results_wanted: int = 15

src/jobspy/scrapers/glassdoor/__init__.py

@@ -5,12 +5,16 @@ jobspy.scrapers.glassdoor
 This module contains routines to scrape Glassdoor.
 """
 import json
-from typing import Optional, Any
+import requests
+from bs4 import BeautifulSoup
+from typing import Optional
 from datetime import datetime, timedelta
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from ..utils import count_urgent_words, extract_emails_from_text
 
 from .. import Scraper, ScraperInput, Site
 from ..exceptions import GlassdoorException
-from ..utils import create_session
+from ..utils import create_session, modify_and_get_description
 from ...jobs import (
     JobPost,
     Compensation,
@@ -66,14 +70,29 @@ class GlassdoorScraper(Scraper):
         jobs_data = res_json["data"]["jobListings"]["jobListings"]
 
         jobs = []
-        for i, job in enumerate(jobs_data):
+        with ThreadPoolExecutor(max_workers=self.jobs_per_page) as executor:
-            job_url = res_json["data"]["jobListings"]["jobListingSeoLinks"][
+            future_to_job_data = {executor.submit(self.process_job, job): job for job in jobs_data}
-                "linkItems"
+            for future in as_completed(future_to_job_data):
-            ][i]["url"]
+                job_data = future_to_job_data[future]
+                try:
+                    job_post = future.result()
+                    if job_post:
+                        jobs.append(job_post)
+                except Exception as exc:
+                    raise GlassdoorException(f'Glassdoor generated an exception: {exc}')
+
+        return jobs, self.get_cursor_for_page(
+            res_json["data"]["jobListings"]["paginationCursors"], page_num + 1
+        )
+
+    def process_job(self, job_data):
+        """Processes a single job and fetches its description."""
+        job_id = job_data["jobview"]["job"]["listingId"]
+        job_url = f'{self.url}/job-listing/?jl={job_id}'
         if job_url in self.seen_urls:
-                continue
+            return None
         self.seen_urls.add(job_url)
-            job = job["jobview"]
+        job = job_data["jobview"]
         title = job["job"]["jobTitleText"]
         company_name = job["header"]["employerNameFromSearch"]
         location_name = job["header"].get("locationName", "")
@@ -89,20 +108,24 @@ class GlassdoorScraper(Scraper):
 
         compensation = self.parse_compensation(job["header"])
 
-            job = JobPost(
+        try:
+            description = self.fetch_job_description(job_id)
+        except Exception as e :
+            description = None
+
+        job_post = JobPost(
             title=title,
             company_name=company_name,
             date_posted=date_posted,
             job_url=job_url,
             location=location,
             compensation=compensation,
-                is_remote=is_remote
+            is_remote=is_remote,
-            )
+            description=description,
-            jobs.append(job)
+            emails=extract_emails_from_text(description) if description else None,
-
+            num_urgent_words=count_urgent_words(description) if description else None,
-        return jobs, self.get_cursor_for_page(
-            res_json["data"]["jobListings"]["paginationCursors"], page_num + 1
         )
+        return job_post
 
     def scrape(self, scraper_input: ScraperInput) -> JobResponse:
         """
@@ -110,6 +133,7 @@ class GlassdoorScraper(Scraper):
         :param scraper_input: Information about job search criteria.
         :return: JobResponse containing a list of jobs.
         """
+        scraper_input.results_wanted = min(900, scraper_input.results_wanted)
         self.country = scraper_input.country
         self.url = self.country.get_url()
 
@@ -143,6 +167,41 @@ class GlassdoorScraper(Scraper):
 
         return JobResponse(jobs=all_jobs)
 
+    def fetch_job_description(self, job_id):
+        """Fetches the job description for a single job ID."""
+        url = f"{self.url}/graph"
+        body = [
+            {
+                "operationName": "JobDetailQuery",
+                "variables": {
+                    "jl": job_id,
+                    "queryString": "q",
+                    "pageTypeEnum": "SERP"
+                },
+                "query": """
+                query JobDetailQuery($jl: Long!, $queryString: String, $pageTypeEnum: PageTypeEnum) {
+                    jobview: jobView(
+                        listingId: $jl
+                        contextHolder: {queryString: $queryString, pageTypeEnum: $pageTypeEnum}
+                    ) {
+                        job {
+                            description
+                            __typename
+                        }
+                        __typename
+                    }
+                }
+                """
+            }
+        ]
+        response = requests.post(url, json=body, headers=GlassdoorScraper.headers())
+        if response.status_code != 200:
+            return None
+        data = response.json()[0]
+        desc = data['data']['jobview']['job']['description']
+        soup = BeautifulSoup(desc, 'html.parser')
+        return modify_and_get_description(soup)
+
     @staticmethod
     def parse_compensation(data: dict) -> Optional[Compensation]:
         pay_period = data.get("payPeriod")
@@ -231,12 +290,11 @@ class GlassdoorScraper(Scraper):
         for job_type in JobType:
             if job_type_str in job_type.value:
                 return [job_type]
-        return None
 
     @staticmethod
-    def parse_location(location_name: str) -> Location:
+    def parse_location(location_name: str) -> Location | None:
         if not location_name or location_name == "Remote":
-            return None
+            return
         city, _, state = location_name.partition(", ")
         return Location(city=city, state=state)
 
@@ -245,7 +303,6 @@ class GlassdoorScraper(Scraper):
         for cursor_data in pagination_cursors:
             if cursor_data["pageNumber"] == page_num:
                 return cursor_data["cursor"]
-        return None
 
     @staticmethod
     def headers() -> dict:

src/jobspy/scrapers/indeed/__init__.py

@@ -22,6 +22,7 @@ from ..utils import (
     extract_emails_from_text,
     create_session,
     get_enum_from_job_type,
+    modify_and_get_description
 )
 from ...jobs import (
     JobPost,
@@ -79,7 +80,7 @@ class IndeedScraper(Scraper):
         if sc_values:
             params["sc"] = "0kf:" + "".join(sc_values) + ";"
         try:
-            session = create_session(self.proxy, is_tls=True)
+            session = create_session(self.proxy)
             response = session.get(
                 f"{self.url}/jobs",
                 headers=self.get_headers(),
@@ -141,7 +142,8 @@ class IndeedScraper(Scraper):
             date_posted = datetime.fromtimestamp(timestamp_seconds)
             date_posted = date_posted.strftime("%Y-%m-%d")
 
-            description = self.get_description(job_url)
+            description = self.get_description(job_url) if scraper_input.full_description else None
+
             with io.StringIO(job["snippet"]) as f:
                 soup_io = BeautifulSoup(f, "html.parser")
                 li_elements = soup_io.find_all("li")
@@ -248,9 +250,7 @@ class IndeedScraper(Scraper):
             return None
 
         soup = BeautifulSoup(job_description, "html.parser")
-        text_content = " ".join(soup.get_text(separator=" ").split()).strip()
+        return modify_and_get_description(soup)
-
-        return text_content
 
     @staticmethod
     def get_job_type(job: dict) -> list[JobType] | None:

src/jobspy/scrapers/linkedin/__init__.py

@@ -4,26 +4,40 @@ jobspy.scrapers.linkedin
 
 This module contains routines to scrape LinkedIn.
 """
+import time
+import random
 from typing import Optional
 from datetime import datetime
 
 import requests
-import time
 from requests.exceptions import ProxyError
-from bs4 import BeautifulSoup
-from bs4.element import Tag
 from threading import Lock
+from bs4.element import Tag
+from bs4 import BeautifulSoup
 from urllib.parse import urlparse, urlunparse
 
 from .. import Scraper, ScraperInput, Site
-from ..utils import count_urgent_words, extract_emails_from_text, get_enum_from_job_type, currency_parser
 from ..exceptions import LinkedInException
-from ...jobs import JobPost, Location, JobResponse, JobType, Country, Compensation
+from ..utils import create_session
+from ...jobs import (
+    JobPost,
+    Location,
+    JobResponse,
+    JobType,
+    Country,
+    Compensation
+)
+from ..utils import (
+    count_urgent_words,
+    extract_emails_from_text,
+    get_enum_from_job_type,
+    currency_parser,
+    modify_and_get_description
+)
 
 
 class LinkedInScraper(Scraper):
-    MAX_RETRIES = 3
+    DELAY = 3
-    DELAY = 10
 
     def __init__(self, proxy: Optional[str] = None):
         """
@@ -57,6 +71,7 @@ class LinkedInScraper(Scraper):
             return mapping.get(job_type_enum, "")
 
         while len(job_list) < scraper_input.results_wanted and page < 1000:
+            session = create_session(is_tls=False, has_retry=True, delay=5)
             params = {
                 "keywords": scraper_input.search_term,
                 "location": scraper_input.location,
@@ -71,44 +86,30 @@ class LinkedInScraper(Scraper):
             }
 
             params = {k: v for k, v in params.items() if v is not None}
-            retries = 0
-            while retries < self.MAX_RETRIES:
             try:
-                    response = requests.get(
+                response = session.get(
                     f"{self.url}/jobs-guest/jobs/api/seeMoreJobPostings/search?",
                     params=params,
                     allow_redirects=True,
                     proxies=self.proxy,
+                    headers=self.headers(),
                     timeout=10,
                 )
                 response.raise_for_status()
 
-                    break
             except requests.HTTPError as e:
-                    if hasattr(e, "response") and e.response is not None:
+                raise LinkedInException(f"bad response status code: {e.response.status_code}")
-                        if e.response.status_code in (429, 502):
-                            time.sleep(self.DELAY)
-                            retries += 1
-                            continue
-                        else:
-                            raise LinkedInException(
-                                f"bad response status code: {e.response.status_code}"
-                            )
-                    else:
-                        raise
             except ProxyError as e:
                 raise LinkedInException("bad proxy")
             except Exception as e:
                 raise LinkedInException(str(e))
-            else:
-                # Raise an exception if the maximum number of retries is reached
-                raise LinkedInException(
-                    "Max retries reached, failed to get a valid response"
-                )
 
             soup = BeautifulSoup(response.text, "html.parser")
+            job_cards = soup.find_all("div", class_="base-search-card")
+            if len(job_cards) == 0:
+                return JobResponse(jobs=job_list)
 
-            for job_card in soup.find_all("div", class_="base-search-card"):
+            for job_card in job_cards:
                 job_url = None
                 href_tag = job_card.find("a", class_="base-card__full-link")
                 if href_tag and "href" in href_tag.attrs:
@@ -123,18 +124,19 @@ class LinkedInScraper(Scraper):
 
                 # Call process_job directly without threading
                 try:
-                    job_post = self.process_job(job_card, job_url)
+                    job_post = self.process_job(job_card, job_url, scraper_input.full_description)
                     if job_post:
                         job_list.append(job_post)
                 except Exception as e:
                     raise LinkedInException("Exception occurred while processing jobs")
 
             page += 25
+            time.sleep(random.uniform(LinkedInScraper.DELAY, LinkedInScraper.DELAY + 2))
 
         job_list = job_list[: scraper_input.results_wanted]
         return JobResponse(jobs=job_list)
 
-    def process_job(self, job_card: Tag, job_url: str) -> Optional[JobPost]:
+    def process_job(self, job_card: Tag, job_url: str, full_descr: bool) -> Optional[JobPost]:
         salary_tag = job_card.find('span', class_='job-search-card__salary-info')
 
         compensation = None
@@ -171,7 +173,7 @@ class LinkedInScraper(Scraper):
             if metadata_card
             else None
         )
-        date_posted = None
+        date_posted = description = job_type = None
         if datetime_tag and "datetime" in datetime_tag.attrs:
             datetime_str = datetime_tag["datetime"]
             try:
@@ -180,21 +182,20 @@ class LinkedInScraper(Scraper):
                 date_posted = None
         benefits_tag = job_card.find("span", class_="result-benefits__text")
         benefits = " ".join(benefits_tag.get_text().split()) if benefits_tag else None
-
+        if full_descr:
             description, job_type = self.get_job_description(job_url)
-        # description, job_type = None, []
 
         return JobPost(
             title=title,
-            description=description,
             company_name=company,
             company_url=company_url,
             location=location,
             date_posted=date_posted,
             job_url=job_url,
-            job_type=job_type,
             compensation=compensation,
             benefits=benefits,
+            job_type=job_type,
+            description=description,
             emails=extract_emails_from_text(description) if description else None,
             num_urgent_words=count_urgent_words(description) if description else None,
         )
@@ -208,12 +209,10 @@ class LinkedInScraper(Scraper):
         :return: description or None
         """
         try:
-            response = requests.get(job_page_url, timeout=5, proxies=self.proxy)
+            session = create_session(is_tls=False, has_retry=True)
+            response = session.get(job_page_url, timeout=5, proxies=self.proxy)
             response.raise_for_status()
         except requests.HTTPError as e:
-            if hasattr(e, "response") and e.response is not None:
-                if e.response.status_code in (429, 502):
-                    time.sleep(self.DELAY)
             return None, None
         except Exception as e:
             return None, None
@@ -227,7 +226,7 @@ class LinkedInScraper(Scraper):
 
         description = None
         if div_content:
-            description = " ".join(div_content.get_text().split()).strip()
+            description = modify_and_get_description(div_content)
 
         def get_job_type(
             soup_job_type: BeautifulSoup,
@@ -292,3 +291,20 @@ class LinkedInScraper(Scraper):
 
         return location
 
+    @staticmethod
+    def headers() -> dict:
+        return {
+            'authority': 'www.linkedin.com',
+            'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
+            'accept-language': 'en-US,en;q=0.9',
+            'cache-control': 'max-age=0',
+            'sec-ch-ua': '"Not_A Brand";v="8", "Chromium";v="120", "Google Chrome";v="120"',
+            # 'sec-ch-ua-mobile': '?0',
+            # 'sec-ch-ua-platform': '"macOS"',
+            # 'sec-fetch-dest': 'document',
+            # 'sec-fetch-mode': 'navigate',
+            # 'sec-fetch-site': 'none',
+            # 'sec-fetch-user': '?1',
+            'upgrade-insecure-requests': '1',
+            'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
+        }

src/jobspy/scrapers/utils.py

@@ -8,6 +8,15 @@ from requests.adapters import HTTPAdapter, Retry
 from ..jobs import JobType
 
 
+def modify_and_get_description(soup):
+    for li in soup.find_all('li'):
+        li.string = "- " + li.get_text()
+
+    description = soup.get_text(separator='\n').strip()
+    description = re.sub(r'\n+', '\n', description)
+    return description
+
+
 def count_urgent_words(description: str) -> int:
     """
     Count the number of urgent words or phrases in a job description.
@@ -29,7 +38,7 @@ def extract_emails_from_text(text: str) -> list[str] | None:
     return email_regex.findall(text)
 
 
-def create_session(proxy: dict | None = None, is_tls: bool = True, has_retry: bool = False):
+def create_session(proxy: dict | None = None, is_tls: bool = True, has_retry: bool = False, delay: int = 1) -> requests.Session:
     """
     Creates a requests session with optional tls, proxy, and retry settings.
 
@@ -51,7 +60,7 @@ def create_session(proxy: dict | None = None, is_tls: bool = True, has_retry: bo
                             connect=3,
                             status=3,
                             status_forcelist=[500, 502, 503, 504, 429],
-                            backoff_factor=1)
+                            backoff_factor=delay)
             adapter = HTTPAdapter(max_retries=retries)
 
             session.mount('http://', adapter)

src/jobspy/scrapers/ziprecruiter/__init__.py

@@ -15,8 +15,8 @@ from concurrent.futures import ThreadPoolExecutor
 
 from .. import Scraper, ScraperInput, Site
 from ..exceptions import ZipRecruiterException
-from ..utils import count_urgent_words, extract_emails_from_text, create_session
 from ...jobs import JobPost, Compensation, Location, JobResponse, JobType, Country
+from ..utils import count_urgent_words, extract_emails_from_text, create_session, modify_and_get_description
 
 
 class ZipRecruiterScraper(Scraper):
@@ -26,6 +26,8 @@ class ZipRecruiterScraper(Scraper):
         """
         site = Site(Site.ZIP_RECRUITER)
         self.url = "https://www.ziprecruiter.com"
+        self.session = create_session(proxy)
+        self.get_cookies()
         super().__init__(site, proxy=proxy)
 
         self.jobs_per_page = 20
@@ -44,12 +46,10 @@ class ZipRecruiterScraper(Scraper):
         if continue_token:
             params["continue"] = continue_token
         try:
-            session = create_session(self.proxy, is_tls=True)
+            response = self.session.get(
-            response = session.get(
                 f"https://api.ziprecruiter.com/jobs-app/jobs",
                 headers=self.headers(),
                 params=self.add_params(scraper_input),
-                timeout_seconds=10,
             )
             if response.status_code != 200:
                 raise ZipRecruiterException(
@@ -106,9 +106,9 @@ class ZipRecruiterScraper(Scraper):
         title = job.get("name")
         job_url = job.get("job_url")
 
-        description = BeautifulSoup(
+        job_description_html = job.get("job_description", "").strip()
-            job.get("job_description", "").strip(), "html.parser"
+        description_soup = BeautifulSoup(job_description_html, "html.parser")
-        ).get_text()
+        description = modify_and_get_description(description_soup)
 
         company = job["hiring_company"].get("name") if "hiring_company" in job else None
         country_value = "usa" if job.get("job_country") == "US" else "canada"
@@ -156,6 +156,11 @@ class ZipRecruiterScraper(Scraper):
             num_urgent_words=count_urgent_words(description) if description else None,
         )
 
+    def get_cookies(self):
+        url="https://api.ziprecruiter.com/jobs-app/event"
+        data="event_type=session&logged_in=false&number_of_retry=1&property=model%3AiPhone&property=os%3AiOS&property=locale%3Aen_us&property=app_build_number%3A4734&property=app_version%3A91.0&property=manufacturer%3AApple&property=timestamp%3A2024-01-12T12%3A04%3A42-06%3A00&property=screen_height%3A852&property=os_version%3A16.6.1&property=source%3Ainstall&property=screen_width%3A393&property=device_model%3AiPhone%2014%20Pro&property=brand%3AApple"
+        self.session.post(url, data=data, headers=ZipRecruiterScraper.headers())
+
     @staticmethod
     def get_job_type_enum(job_type_str: str) -> list[JobType] | None:
         for job_type in JobType:
@@ -195,12 +200,16 @@ class ZipRecruiterScraper(Scraper):
     @staticmethod
     def headers() -> dict:
         """
-        Returns headers needed for ZipRecruiter API requests
+        Returns headers needed for requests
         :return: dict - Dictionary containing headers
         """
         return {
-            'Host': 'api.ziprecruiter.com',
+            "Host": "api.ziprecruiter.com",
-            'accept': '*/*',
+            "accept": "*/*",
-            'authorization': 'Basic YTBlZjMyZDYtN2I0Yy00MWVkLWEyODMtYTI1NDAzMzI0YTcyOg==',
+            "x-zr-zva-override": "100000000;vid:ZT1huzm_EQlDTVEc",
-            'Cookie': '__cf_bm=DZ7eJOw6lka.Bwy5jLeDqWanaZ8BJlVAwaXrmcbYnxM-1701505132-0-AfGaVIfTA2kJlmleK14o722vbVwpZ+4UxFznsWv+guvzXSpD9KVEy/+pNzvEZUx88yaEShJwGt3/EVjhHirX/ASustKxg47V/aXRd2XIO2QN; zglobalid=61f94830-1990-4130-b222-d9d0e09c7825.57da9ea9581c.656ae86b; ziprecruiter_browser=018188e0-045b-4ad7-aa50-627a6c3d43aa; ziprecruiter_session=5259b2219bf95b6d2299a1417424bc2edc9f4b38; zva=100000000%3Bvid%3AZWroa0x_F1KEeGeU'
+            "x-pushnotificationid": "0ff4983d38d7fc5b3370297f2bcffcf4b3321c418f5c22dd152a0264707602a0",
+            "x-deviceid": "D77B3A92-E589-46A4-8A39-6EF6F1D86006",
+            "user-agent": "Job Search/87.0 (iPhone; CPU iOS 16_6_1 like Mac OS X)",
+            "authorization": "Basic YTBlZjMyZDYtN2I0Yy00MWVkLWEyODMtYTI1NDAzMzI0YTcyOg==",
+            "accept-language": "en-US,en;q=0.9",
         }