ML Engineer, ML Platform, Tokyo
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Overview
Only applicants residing in Japan are eligible to apply.
Overview
This position leads the implementation and operation of credit assessment models for Money Forward Kessai (MFK) and the Digital Bank project.
You will migrate models from the PoC phase to a bank-grade production environment, establish continuous quality management through MLOps, and drive implementation of fairness and transparency based on the principles of Responsible AI.
Background
Initial validation of the credit assessment models is largely complete. As the next phase, we are accelerating both “integration into banking systems” and “building a strict operational setup.” As a hands-on leader, you will be expected to drive solutions to the following challenges:
Migration to production: Smoothly migrate from the verification environment (Databricks) to the production environment (SageMaker), and build a robust CI/CD pipeline that removes uncertainty.
Bank-grade quality assurance: Implement XAI (e.g., SHAP, counterfactuals) to explain lending decisions, and detect/mitigate model bias to ensure fairness.
Efficient processing of large-scale data: Optimize training and inference cycles by introducing distributed learning/processing (e.g., pandas UDF / Spark) leveraging the characteristics of accounting transaction data.
Responsibilities
- You will lead technology selection and hands-on implementation in the field.
- Design and implement ML workflows
- Build an end-to-end ML pipeline integrating Databricks and Amazon SageMaker.
- Implement and enhance credit assessment models
- Implement scalable model training using large datasets (e.g., Parquet).
- Introduce fairness and explainability (XAI)
- Implement bias evaluation using statistical methods (e.g., statistical parity) and algorithms to visualize decision rationales.
- Model operation and monitoring
- Continuously monitor performance degradation and environmental changes (concept drift), and operate retraining workflows.
Required Skills
- Experience operating ML models in production
- Practical experience across the full lifecycle from development to deployment and post-release monitoring.
- Experience in data processing and development with Python
- Practical use of ML libraries (pandas, scikit-learn, etc.) and backend frameworks.
- Technical leadership experience
- Experience conducting design reviews and setting technical direction in a small team.
- Fundamental data engineering knowledge
- Experience using RDBMS/SQL and building data pipelines on cloud platforms (e.g., AWS).
Preferred Skills
- Knowledge of AI fairness and explainability
- Understanding of fairness metrics and use of tools such as SageMaker Clarify.
- Experience with distributed processing at scale
- Performance tuning experience using Apache Spark, pandas UDF, etc.
- Knowledge of the financial domain
- Experience developing systems in banking/credit operations, or in environments compliant with security standards such as FISC.
- Language Requirements
- Business level Japanese (equivalent to JLPT N2 or above)
- Basic business level English (equivalent to TOEIC 700 or above)
- If you do not have a qualification equivalent to TOEIC 700 or above, you will be required to take a company-designated test during the selection process.
- If you have other qualifications or experiences that demonstrate your English proficiency, please let us know.
- Examples include Eiken Pre-1st Grade, Eiken 2nd Grade (Eiken CSE Score 1950 or above), TOEFL iBT 60 or above, IELTS 5.0 or above, Cambridge English FCE, etc.
- ※Please note that the interviews in the selection process will be conducted in Japanese.
- Who We’re Looking For
- Strong resonance with Money Forward's vision
- Passionate about being a fan of our services and contributing to their growth
- Respects governance and compliance as an engineer supporting banking services, and pursues robust implementations.
- Able to break down abstract business requirements into concrete technical tasks and drive projects autonomously.
- Shares expertise and contributes to raising the team’s overall technical standard.
- Technology Stack
- Platform: AWS (SageMaker, Lambda, ECS, S3, etc.), Databricks
- Data: Python (FastAPI, etc.), SQL, Apache Airflow / Step Functions
- DevOps: Terraform, GitHub Actions, CodePipeline
- Communication: Slack, Notion
- Work Environment
- At Money Forward, we provide an environment where we can create world-class services together, and we are looking forward to welcoming you.
- Provided PC Specs: We provide PCs equipped with the latest CPUs (MacOS or Windows). Custom-made PCs tailored to business requirements and replacements with the latest OS are also possible.
- Systems to Enhance the Development Environment: Peripheral devices necessary for work (such as displays, mice, keyboards) can be purchased as office supplies. Generally, you can choose from standard products (catalog), and if conditions are met, you can apply for non-standard products as well.
- Money Forward Library: We have a library system where you can freely borrow books, ranging from technical books to management books. Desired books can be purchased at the company's expense.
- Referral Driven: We cover the cost of recruitment meals. There is a referral reward system.
- Conference Participation Support: The company partially covers participation in domestic and international conferences, such as RubyKaigi and Google I/O."
Quick Facts
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