JD in Japanese follows. 英文の後に和文JDをご覧いただけます。
Introduction
Circulate all forms of value to unleash the potential in all people
"What can I do to help society thrive with the finite resources we have?" The Mercari marketplace app was born in 2013 out of this thought by our founder Shintaro Yamada as he traveled the world. We believe that by circulating all forms of value, not just physical things and money, we can create opportunities for anyone to realize their dreams and contribute to society and the people around them. Mercari aims to use technology to connect people all over the world and create a world where anyone can unleash their potential.For more information about Mercari Group’s mission, see Mercari's Culture Doc.
Equal Opportunity Hiring
Here at Mercari, we work to realize a world in which no one’s potential is limited by their background and everyone has the opportunity to freely create value. We also firmly believe that a mindset of Inclusion & Diversity is essential for us to achieve our mission.
This, of course, extends to our hiring practices as well. Mercari is committed to eliminating discrimination based on age, gender, sexual orientation, race, religion, physical disability, and other such factors so that anyone who shares our mission and values can join us, regardless of their background. For more details, please read our I&D Statement.
Position Overview
Work Responsibilities
- Machine learning engineers working in the Recommendation domain develop the functions and services of the marketplace app Mercari through the development and maintenance of machine learning systems like Recommender systems while leveraging necessary infrastructure and platform tools. Mercari is actively applying advanced machine learning technology to provide a more convenient, safer, and more enjoyable marketplace. Machine learning engineers use the cloud and Kubernetes to operate and improve machine learning systems.
Unique and Bold Challenges
- Develop and optimize machine learning algorithms and models to enhance the recommendation system to improve discovery experience of users
- Collaborate with cross-functional teams and product stakeholders to gather requirements, design solutions, and implement features that improve user engagement
- Conduct data analysis and experimentation with large-scale data sets to identify patterns, trends, and insights that drive the refinement of recommendation algorithms
- Utilize machine learning frameworks and libraries to deploy scalable and efficient recommendation solutions.
- Monitor system performance and conduct A/B testing to evaluate the effectiveness of features.
- Continuously research and stay updated on advancements in machine learning techniques and recommend innovative approaches to enhance recommendation capabilities.
Required Skills
- Strong experience demonstrating development and delivery of end-to-end machine learning solutions starting from experimentation to deploying models, including backend engineering and MLOps, in large scale production systems.
- Experience using common machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NumPy, pandas)
- Deep understanding of machine learning and software engineering fundamentals.
- Strong analytical and problem-solving skills
- Basic knowledge and skills related to monitoring system, logging, and common operations
- Communication skills to carry out projects in collaboration with multiple teams and stakeholders
- Possess strong product engineering mindset
Preferred skills
- Experience developing Recommender systems utilizing large-scale data sets
- Functional development and bug fixing skills necessary to improve system performance and reliability
- Using container technology such as Docker and Kubernetes
- Using cloud platforms (AWS, GCP, Microsoft Azure, etc.)
- Microservice development and operation experience with Docker and Kubernetes
- Utilizing deep learning models in production
Language Requirements
- Japanese: Basic (CEFR - A2) [Preferred]
- English: Basic (CEFR – A2)
*For details about CEFR, see here
Related Articles
- https://ai.mercari.com/articles/ai/recsys2023/
- Conversation between Machine Learning Engineers. Meet @ lain21 and @ chica #WeMakeMercari
- https://speakerdeck.com/shyaginuma/merukarihomuhua-mian-niokerurekomendogai-shan-shi-li-long-tailwokao-lu-sitaci-shu-kuo-zhang
Working Conditions
Employment Status
Full-time
- Probationary period: First 3 months after joining the company. (During this period your contract conditions will be the same as that of a permanent employee.)
Office
Roppongi
- Smoking is prohibited within our offices
- Mercari has introduced a work style policy called “Your Choice.” Each member is free to choose whether they want to work in the office or work fully remote. *Exceptions made for certain kinds of work.
Work Hours
- Full flextime (no “core time” or “flex time”)
*Does not apply to all positions
Holidays
- Two days off per week (as well as national holidays, New Year's break, etc.)
- Paid leave, congratulatory and bereavement leave, relax days, sick leave
Salary
- Annual salary paid in 12 monthly installments (including fixed overtime allowance)
- Based on skills, experience, and abilities
- Reviewed twice a year
Benefits
- Complete health and social insurance
- Incentive program
- Support systems, including those that benefit the employee’s family members
*See this page for details.
Support
- Relocation support
- Language learning support
- Translation/interpretation support
*See this page for details.
Media
Corporate Sites
- Mercari, Inc.
- Merpay, Inc. [Japanese]
- Mercoin, Inc.
- Mercari US
Owned Media
- Mercari Careers
- Mercari Engineering Portal
- Mercan - Introduces the people that make Mercari
- Mercari US Careers
- Mercari US Blog
- Mercari AI
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