Say Goodbye to Annoying LinkedIn Job Alerts: A Step-by-Step Guide with Easy Code Examples

Table of content

  1. Introduction
  2. Why Are LinkedIn Job Alerts Annoying?
  3. How Can You Stop Receiving Annoying LinkedIn Job Alerts?
  4. Step 1: Finding the LinkedIn API Endpoint
  5. Step 2: Authenticating Your Account Using OAuth 2.0
  6. Step 3: Writing the Code to Stop Receiving LinkedIn Job Alerts
  7. Step 4: Setting Up a Cron Job to Automatically Run the Script
  8. Conclusion


If you've ever used LinkedIn to search for jobs, you've likely been bombarded with job alerts that just aren't relevant to your interests or skill set. This can be incredibly frustrating, especially when you're trying to find a job that's the right fit for you. Fortunately, there is a solution to this problem: machine learning.

By using machine learning algorithms, companies like LinkedIn can analyze your behavior and interactions on the site to understand your interests and preferences. This allows them to personalize your job alert recommendations so that you only receive notifications for jobs that match your skills and experience.

In this article, we'll explore how machine learning works and how it can be used to improve the job search experience on LinkedIn. We'll provide step-by-step instructions on how to use code to adjust your job alert settings, making it easy to say goodbye to annoying job alerts and hello to a more streamlined job search process. Whether you're a job seeker or a recruiter, understanding machine learning and its applications can help you make the most out of your time on LinkedIn.

Why Are LinkedIn Job Alerts Annoying?

LinkedIn job alerts can sometimes be annoying for users, especially if they receive too many notifications. While it's great to be informed of new job opportunities, it can also become overwhelming and lead to users ignoring important alerts. Additionally, these alerts may not always be relevant or tailored to a user's specific job search criteria, leading to frustration and wasted time.

One reason why LinkedIn job alerts can be annoying is due to the algorithm used to generate them. The LinkedIn algorithm uses machine learning to analyze a user's job preferences, search history, and engagement with job postings to create personalized job alerts. However, this process is not always perfect, and users may receive alerts for jobs that do not match their criteria or interests.

Furthermore, the frequency of LinkedIn job alerts can also be bothersome. Users may receive alerts multiple times a day, causing notification fatigue and making it difficult to distinguish important alerts from less significant ones. This can lead to users disabling all job alerts, thus missing out on potentially valuable job opportunities.

Overall, while LinkedIn job alerts can be helpful in finding new job opportunities, the algorithm used to generate them can sometimes be flawed, leading to irrelevant or too-frequent notifications that can be bothersome for users. Fortunately, there are steps that can be taken to optimize job notifications and make the process more user-friendly, such as utilizing code examples to create customized alerts.

How Can You Stop Receiving Annoying LinkedIn Job Alerts?

If you're tired of being bombarded by LinkedIn job alerts, there are a few steps you can take to stop them. Here's how:

  1. Log in to your LinkedIn account and go to your "Settings & Privacy" page.
  2. Click on the "Communications" tab.
  3. Under the "Channels" section, click on "Job alerts" to expand the options.
  4. Turn off the toggle switch next to "Email" to stop receiving job alerts via email.
  5. If you still want to receive job alerts, you can customize the frequency and types of alerts you receive. For example, you can choose to receive only relevant job alerts or limit the frequency of notifications.

Alternatively, you can use code examples to automatically remove job alerts from your inbox. For example, you can create a filter in Gmail or use a Python script to delete job alert emails as soon as they arrive.

By following these steps, you can regain control of your inbox and stop being bombarded by unwanted job alerts.

Step 1: Finding the LinkedIn API Endpoint

Finding the LinkedIn API Endpoint

The first step in saying goodbye to annoying LinkedIn job alerts is to locate the LinkedIn API endpoint. The API endpoint is the URL that defines the location where data can be accessed and extracted from LinkedIn's servers. Follow these steps to find the LinkedIn API endpoint:

  1. Log in to LinkedIn and go to "Developer Dashboard": To access the LinkedIn API endpoint, you must first create a developer account on LinkedIn. Once you have logged in, go to the "Developer Dashboard" and click on the "Products" tab.

  2. Select the "Marketing Developer Platform": From the "Products" tab, select the "Marketing Developer Platform". This platform provides access to LinkedIn's advertising tools and APIs.

  3. Create a new app: To create a new app, click on the "Create App" button in the upper right-hand corner of the page. Follow the instructions to provide a unique name and description for your app.

  4. Get the OAuth 2.0 access token: After creating the app, you will need to obtain an OAuth 2.0 access token to access the LinkedIn API endpoint. Follow the instructions provided by LinkedIn to obtain the access token.

  5. Locate the API endpoint: Once you have obtained the access token, you can locate the API endpoint by visiting the LinkedIn Developer Documentation and selecting the API that you want to use. From there, you can find the API endpoint URL and use it to start accessing the data you need.

By following these steps, you can easily locate the LinkedIn API endpoint and begin using it to extract the data you want without having to deal with annoying job alerts.

Step 2: Authenticating Your Account Using OAuth 2.0

To authenticate your LinkedIn account, you'll need to use OAuth 2.0, an industry-standard protocol for authentication and authorization. OAuth 2.0 allows you to give permission to third-party applications to access your LinkedIn account without giving them your password or other sensitive information.

To use OAuth 2.0, you'll need to register your application with LinkedIn and obtain a Client ID and a Client Secret. You can do this by creating a new app in the LinkedIn Developer Console.

Once you have your Client ID and Client Secret, you'll need to create an authorization URL that will prompt the user to grant permission to your application. The URL should include your Client ID, the desired scope of access (e.g. read-only access to the user's profile), and a redirect URL where the user will be sent after granting permission.

After the user grants permission, LinkedIn will send an authorization code to your redirect URL. You can then exchange this code for an access token, which can be used to make API calls on behalf of the user.

To authenticate your LinkedIn account using OAuth 2.0, follow these steps:

  1. Register your application with LinkedIn and obtain a Client ID and a Client Secret.
  2. Create an authorization URL that prompts the user to grant permission to your application.
  3. Handle the authorization code returned by LinkedIn and exchange it for an access token.
  4. Use the access token to make API calls on behalf of the user.

By authenticating your LinkedIn account using OAuth 2.0, you can ensure that your account is secure while still allowing third-party applications to access your profile and data.

Step 3: Writing the Code to Stop Receiving LinkedIn Job Alerts

Now that we have identified the key elements of the LinkedIn job alerts and have determined how our code will interact with them, it's time to write the code.

First, we will need to use a web scraping tool like Beautiful Soup or Scrapy to crawl through our LinkedIn account and find the relevant job alert pages. Once we have identified the correct pages, we can use Python code to interact with the website and stop receiving these alerts. Here's an example of the code we could use:

from selenium import webdriver

# Load the LinkedIn login page using Selenium
driver = webdriver.Chrome()

# Enter your login credentials and click the sign-in button

# Navigate to the webpage for your job alerts

# Use Selenium to find the switch elements on the page and turn them off
switches = driver.find_elements_by_class_name("switch")
for switch in switches:
    if switch.get_attribute("aria-checked") == "true":

# Close the browser window

This code uses the Selenium package to simulate a user logging into their LinkedIn account and navigating to the job alerts page. It then finds all the switches on the page (which correspond to the job alert settings) and turns them off.

Note that this code is just an example, and may need to be modified to work with your specific LinkedIn account and job alert settings. Also, while web scraping can be a powerful tool, it is important to use it responsibly and ethically, as it can sometimes violate website terms of service or even be illegal in certain jurisdictions.

Overall, writing code to stop receiving LinkedIn job alerts may seem daunting, but with the right tools and techniques, it can be a simple and straightforward task. By leveraging the power of Python and web scraping, we can streamline our online experiences and make sure we only see the content that is most relevant to us.

Step 4: Setting Up a Cron Job to Automatically Run the Script

Now that you have your script ready, it's time to automate the process of running it. This can easily be achieved using a cron job, which is a scheduling utility in Unix-like operating systems.

Here's how to set up a cron job to run your script at a specific time:

  1. Open your terminal and type crontab -e to open your crontab file.
  2. Add a new line at the bottom of the file with the following syntax:

* * * * * /path/to/python /path/to/

This will run your script every minute. If you want to run it at a different frequency, you can use the following syntax:

* * * * * /path/to/python /path/to/
- - - - -
| | | | |
| | | | ----- Day of the week (0 - 7) (Sunday is both 0 and 7)
| | | ------- Month (1 - 12)
| | --------- Day of the month (1 - 31)
| ----------- Hour (0 - 23)
------------- Minute (0 - 59)

  1. Save and close the file.

That's it! Your script will now run automatically at the specified time. This is a great way to ensure that you never miss an opportunity on LinkedIn, without having to check your emails or notifications every few minutes.

In conclusion, using machine learning to filter job alerts on LinkedIn can save you time and frustration. By following these easy steps and setting up a cron job to automatically run your script, you can say goodbye to annoying job alerts and focus on the opportunities that matter most to you.


In , machine learning has revolutionized the way we interact with technology and our daily lives. Whether it's personalized recommendations on Amazon or LinkedIn job alerts, machine learning algorithms analyze vast amounts of data to provide us with tailored experiences that meet our needs and preferences. As we continue to develop more sophisticated algorithms and data analysis tools, the possibilities for how we use machine learning are endless. From healthcare to finance to social media, we can expect machine learning to play an increasingly vital role in shaping our world. It's an exciting time for innovation and discovery, and machine learning is at the forefront of this new era. With the right tools and resources at our disposal, we can unlock the full potential of this powerful technology and create a better future for ourselves and the world around us.

As a developer, I have experience in full-stack web application development, and I'm passionate about utilizing innovative design strategies and cutting-edge technologies to develop distributed web applications and services. My areas of interest extend to IoT, Blockchain, Cloud, and Virtualization technologies, and I have a proficiency in building efficient Cloud Native Big Data applications. Throughout my academic projects and industry experiences, I have worked with various programming languages such as Go, Python, Ruby, and Elixir/Erlang. My diverse skillset allows me to approach problems from different angles and implement effective solutions. Above all, I value the opportunity to learn and grow in a dynamic environment. I believe that the eagerness to learn is crucial in developing oneself, and I strive to work with the best in order to bring out the best in myself.
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