Recruitment Background & Expected Role

CADDi is on a mission to "Unleash the potential of manufacturing."

We operate "CADDi DRAWER," a cloud-based system that supports digital transformation centered on the use of drawings, which are the most essential data in the manufacturing industry.

One of the issues we are currently facing in the development of this rapidly growing product is developer productivity. The inefficiencies in the development environment have become more noticeable, and they are becoming a hindrance to the development of features and a degradation of the developer experience. As an activity to resolve these issues, the need for Platform Engineering is increasing.

There are various interpretations and approaches to Platform Engineering in the world, but here, we will be working on providing tools and infrastructure to increase the development velocity by “separating concerns.”

Examples of anticipated tasks (not limited to)

Among these, in this post, you will be working on the following tasks

• Build API and batch execution environments for running machine learning model inference, as well as deployment environments using CI/CD.
• Implement monitoring, performance tuning, and other improvements to enhance site reliability in production environments.
• Optimize the cost of inference and training platforms.
• Create the deployment and operation processes based on input from the modeling and platform teams.
• Actively experiment with new ML, MLOps, and infrastructure tools, quickly validating ideas through proof-of-concepts and applying what works to real company products.

* Besides the team we are recruiting for this time, you may be assigned to other teams depending on your experience and preferences. (In that case, we would be happy to discuss this with you at the interview.)

* After joining the company, your role may change due to organizational growth or an individual's career perspective.

Interest and experience gained from this position

• Close collaboration with ML engineers and product management members, allowing you to expand your responsibilities depending on your interests and initiative.
• The fun of integrating a complex domain into a system
• Experience in solving difficult problems with highly motivated team members
• Experience in contributing to the scale of a product with technical skills
• Experience in developing products that are deployed globally
• Experience in providing value to society through the development of products that change

Organization

• Currently, the Analysis Platform team focuses on supporting modeling, ML serving, and operational workflows. Looking ahead, we aim not only to reduce the cognitive and operational burden around machine learning, but also to build a robust foundation for ML verification—ultimately shortening the lead time needed to validate business value.
• To achieve this, the Analysis Platform team will expand its scope beyond traditional MLOps. We will take on broader challenges such as improving annotation efficiency and developing mechanisms that enable faster and more reliable value validation.