About the job
# Join Our Team
- As a Machine Learning Engineer (MLE) for Home Recommendations, you will play a crucial role in optimizing the recommendation strategies for various content, services, promotions, and messages within the Toss app using machine learning.
- Your work will involve precisely modeling the effectiveness of different content at various user touchpoints such as the top of the Toss home screen, banners after money transfers, and push notifications, while continuously improving actual recommendation outcomes.
- To enhance the quality of recommendations across diverse areas, you will focus on quantitative performance forecasting and the design of sophisticated exposure strategies.
# Responsibilities
- Design and enhance recommendation models for content, services, and promotions displayed across the Toss app.
- Develop models to predict appropriate recommendations and responses (e.g., CTR) based on user behavior, timing, and context.
- Engage in iterative improvement of recommendation systems through feature discovery, experimental design, and performance evaluation.
- Optimize exposure strategies for various recommendation areas and push messages based on user responses, quantitatively measuring actual performance.
# Who We Are Looking For
- Individuals with practical experience dealing with recommendation systems, ranking, and personalization modeling are essential.
- A background in designing and experimenting with CTR prediction, user response prediction, and ranking modeling is crucial.
- Experience in feature engineering and model performance enhancement using user behavior data is required.
- Proficiency in major ML frameworks such as PyTorch, TensorFlow, and LightGBM is necessary.
- Candidates who have improved actual service performance through iterative experimentation and quantitative analysis will be preferred.
- The ability to clearly define problems and explain them technically while collaborating with diverse teams is highly valued.
# Resume Tips
- Please detail any projects you have worked on that have had significant organizational impact.
- Specify the problems you defined, the approaches you chose, the experimental methods, and how you improved performance in modeling-centric projects.
- Highlight any processes where you quantitatively solved problems through iterative experiments and performance analysis.
# Application Process
- Application Submission > 1st Technical Interview (Coding) > 2nd Technical Interview > Cultural Fit Interview > Reference Check > Compensation Negotiation > Final Acceptance
- The first technical interview will include a simple coding test, resume check, and a basic ML knowledge assessment.
- The second technical interview will involve in-depth technical questions and discussions on ML system design.
# A Message for Future Colleagues
- "This role is not just about modeling; it's about making an impact on the business."
- The most satisfying aspect of working at Toss is that we do more than just modeling.
- Previously, the work was limited to inputting data into existing models and evaluating performance, but now we focus on how to incorporate unaggregated data into our models.
- It is rewarding to contribute to running a super app based on a deep understanding of users, moving beyond just analyzing and modeling financial-related data!

