Mercari is a marketplace app that makes it easy for people to safely sell and ship their things. Having been downloaded over 100 million times, it is now among the largest peer-to-peer selling platforms globally.
Though we have over 1,800 employees, we still have a startup culture, where we encourage people to come up with big, crazy ideas, and to not be afraid of failure. Because the company is rapidly growing, you can set your own path, and there is enough transparency to allow our members to do so. For instance, at our all-hands meetings, every single member is encouraged to ask questions directly to our executive team.
We're a Japanese company, but are building a global work culture, and so we provide a great opportunity to experience a blend of Japanese and international culture. We relocate developers from around the world to join our team, and provide translation and interpretation to smooth communication between members.
We want our employees to be able to give 100% both inside and outside of the office, and our benefits reflect this. These include providing language education, financial support for childcare, and allowing you to pursue paid side gigs outside of working with us.
About the position
As a Machine Learning Engineer at Mercari, you will be responsible for analyzing C2C purchasing data in order to provide our users with a better, safer e-commerce experience.
- Improving customer's behavior prediction.
- Carrying out anomaly detection.
- Providing a recommendation engine.
- Optimizing item search algorithms.
- Developing a system for image recognition.
- Experience and expertise in machine learning for more than 3 years.
- Experience working on end-to-end development of machine learning applications, including prototyping, model evaluation, and error analysis, using Python or other languages.
- Experience developing web services using Flask, Django, or other web frameworks.
- Fundamental knowledge of RDBMS and SQL.
- Experience leading various projects and, proposing solutions to tackle problems, based on knowledge of domain-specific services and systems.