Overview

Money Forward is developing a variety of services for individuals and corporations to realize our vision, "Becoming the financial platform for all". In addition, we are working to promote the effective use of data. In order to further address our customers' needs in the future, we are actively strengthening our development system using AI/ML technology for the main services of each department.

We are looking for a passionate MLOps Platform Engineer to drive the operation and implementation strategies of AI/ML projects alongside our ML engineering development team. As part of our team, you will aid with the integration and optimization of ML technologies to enhance user experiences and contribute to Money Forward's numerous services.

Attractive Points

In this role, you will be at the forefront of the latest technologies in container orchestration, cloud services, and CI/CD pipelines to enable efficient development, training and deployment of ML models.

You will have the autonomy to design and implement optimization strategies, operate and maintain a scalable robust infrastructure tailored for ML projects, and empower ML engineers throughout the MLOps cycle.

Alongside our technical team of talented experienced ML engineers, you will also have the opportunity to contribute to the MLOps cycle, gaining valuable insights in a diverse and dynamic environment.

Responsibility

• As an MLOps Platform Engineer, you will play a critical role by enabling our team of ML engineers to develop, train and deploy ML projects efficiently using the latest technologies in container orchestration, cloud services, CI/CD pipelines for data collection, model training and monitoring in production
• Building and maintaining a scalable infrastructure to execute ML projects, while committed to results and user value
• Develop, design, maintain and manage container orchestration using Kubernetes
• Design and execute strategies for GPU optimization, prediction servers, data and training pipelines while ensuring efficient use
• Design and build inference platforms while ensuring reliability and high performance
• Provision and monitor infrastructure resources
• Build and maintain ML workflows and pipelines
• Deploy and maintain monitoring services for observability
• Ensure compliance with security best practices

Qualification

• Bachelor's degree in Computer Science, engineering or related field
• 3+ years building core infrastructure for ML projects
• 2+ years of experience implementing AI/ML algorithms, refining and improving models, and integrating them into production services
• Experience in managing, designing, implementing and maintaining robust ML infrastructure to support development and inference workloads, ML workflows, training pipelines and versioning
• Experience building and scaling machine learning infrastructure
• Experience with AWS cloud services
• Experience with Kubernetes to deploy and manage containerized applications with high availability and performance
• Experience in running and scaling inference clusters
• Experience with TerraGrunt or TerraForm, IaC and CI/CD practices
• Comfortable taking over legacy projects for operation and maintenance
• Proficiency in programming (Python or Go)
• Excellent problem-solving skills and ability to work in a dynamic environment
• Effective communication skills to collaborate with technical and nontechnical members

Nice-to-have

• Master's degree in Computer Science, engineering or related field
• Proficiency on KubeFlow and MLFlow for workflows and pipelines
• Experience in designing, developing and operating large-scale AI/ML systems
• Certifications in AWS(MLS-C01), Kubernetes(CKA) or relevant technologies
• Experience with additional cloud services
• Contributions to open-source projects
• Experience in working to improve model performance, including AI/ML model refinement and fine-tuning
• Knowledge of data security standards such as handling personal information, financial/accounting data, PCI DSS, etc., and experience in designing, developing, and operating systems by these requirements

Required language skills

• Business level English or Native Japanese with intermediate English

Those who fit

• A shared belief in Money Forward's Mission/Vision/Values/Culture
• Able to communicate proactively across organizational boundaries and resolve complex inter-organizational issues on their initiative
• Able to recognize and leverage the potential in Money Forward's data
• Feel the joy and satisfaction of solving business problems with AI/ML technology

Tech Stack

• AI/ML: SageMaker, TensorFlow, PyTorch, Kubeflow etc.
• AWS: SageMaker, EKS, ECS, Lambda, Step Functions etc.
• Middleware: Docker, Terraform, Kubernetes
• Programming Language: Python, Go etc.

Tool

• Groupware: Google Workspace
• Repository Management: GitHub
• CI/CD: CircleCI, GitHub Actions
• Monitoring: Datadog, Grafana
• Communication: Slack
• Ticket Management: Jira

Location, Hybrid Work

• As a standard practice, a minimum of 2 days work from office attendance is mandatory, designated as team office days. Additionally, employees are encouraged to spend 3 or more days in the office.
• The specific "team office days" may vary depending on the assigned team.
• This policy may be subject to change based on the company's needs and work circumstances.

Working hours

Flexible Working Hours (No core time)

Vacations

• Two days off per week (Saturday and Sunday)
• Japanese national holidays
• Paid holiday: 10 days (first year) *Number of paid holidays increases (+1 day) every year up to 20 days a year.
• Summer vacation days: 3 days
• Winter vacations days: 2 days

Benefit

• Health insurance
• Employee stock ownership plan
• Full transportation coverage
• The latest computer (if considered necessary for work, limitless upgrade on specs and purchases of peripheral equipment are allowed.)
• Seminar participation support
• Book purchases
• Copyright of OSS belongs to individuals

Relocation Support for Overseas Candidates

• Working Visa
• Flight ticket to Japan
• Relocation Bonus
• Temporary fully furnished apartment for the first month

Selection Process

Casual interview/Document Screening

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Coding assignment

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First interview

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Second interview

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Final interview *Reference checks may be required before or after the interview.

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Job offer and Meeting

*The selection process may change

■Reference checks

Money Forward may ask you for the reference checks using an online service, called "back check".

Mutual understanding is limited in the selection process only. Therefore, we would like to refer to the information about you from your supervisor and colleagues working together at the current or previous company so that we can make a more reliable match and to lead to your early success after joining our company.

*No pass/fail decision will be made on the basis of the reference checks only.

*The fact that you are applying for us will not be disclosed to your references.