IRL is the leading group messaging social network that brings people together through events and shared experiences. With its wide range of in-app offerings, including personalized event suggestions using artificial intelligence, calendar integrations, messenger and private group pages, IRL deepens everyone's ability to connect through organic community-building and engaging social interaction. 

Based on humans' natural group orientation and essential need to communicate, especially in the post-COVID era, IRL is building the future of interaction by translating these inherent needs onto one platform and encouraging users to do more together.

IRL is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Our benefits for U.S. employees:
  • IRL pays 99% of the monthly premium for platinum and gold PPO medical insurance, plus vision and dental coverage, for employees and their spouses, partners & children.
  • All employees receive 25 days of Personal Time Off every calendar year plus 5 Floating Holidays.
  • Sick leave honor system: Employees can take up to 2 consecutive days per illness without having to take PTO.
  • Remote Work Set Up Stipend for new employees of up to $7,500 for engineers and $5,000 for non-engineers.
  • All-Purpose Stipend of $4,800 per year for ongoing remote work expenses plus an incredible range of products and services.
  • Celebrations Stipend: Employees may receive up to $1,200 per year for personal and family events planned through the IRL app.
  • Student Loan Repayment: IRL will contribute up to $437.50 per month, with a maximum lifetime benefit of $31,500, for the repayment of employee student loans.
  • Professional Development: IRL will contribute up to $3,000 annually, with a maximum lifetime benefit of $12,000, for expenses associated with employee professional development.
  • 401(k) Retirement Plan (coming Fall 2021) with 100% match on up to 6% of employee salary.

IRL’s AI Team is looking for a machine learning engineer to help us build end-to-end machine learning solutions for millions of users.

In this position, you will work closely with product teams to convert their wildest ideas into reality with AI.

If you're interested in developing highly scalable machine learning systems from the ground up, let's chat!

 

Day-to-day responsibilities include:

  • Talk to product teams to identify machine learning applications
  • Design overall machine learning system for each application
  • Collaborate with data engineers to collect data
  • Design features and train machine learning models
  • Deploy machine learning models in a scalable fashion

 

You should apply for this role if you have the following qualifications:

  • Excellent software engineering skills
  • Knowledgeable about machine learning techniques (overfitting vs underfitting, tradeoffs between different models...)
  • Experience in one of the major machine learning packages (PyTorch, Tensorflow, Keras…)
  • Proficient in Python
  • Experience in AWS core services

 

The focus and potential success of this role:

 

Recommendation focus:

Success after 3 months would include objectives like:

  • Design and deploy a machine learning model for recommendation.

Success after 6 months would include objectives like:

  • Design and deploy an automated and maintainable machine learning system that regularly re-trains on new data for recommendation.

Success after 12 months would include objectives like:

  • Show significant improvement in business metrics with deployed machine learning systems, such as 30% relative increase in click-through rate.

 

Chat focus:

Success after 3 months would include objectives like:

  • Deploy and deploy a machine learning model for chat, such as spam detection.

Success after 6 months would include objectives like:

  • Design and deploy an automated and maintainable machine learning system that regularly re-trains on new data.

Success after 12 months would include objectives like:

  • Show significant improvement in business metrics with deployed machine learning systems, such as 30% decrease in spam rate.

 

Content focus:

Success after 3 months would include objectives like:

  • Deploy a machine learning model to scrape contents to create events and groups.

Success after 6 months would include objectives like:

  • Design and deploy an automated and maintainable machine learning system that regularly re-trains on new data.

Success after 12 months would include objectives like:

  • Show significant improvement in business metrics with deployed machine learning systems, such as 30% relative increase in the number of groups users joined.

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