Software Engineer, AI/ML Infrastructure (Remote)

★Software Engineer [AI/ML Infrastructure] | Portal Site, App Development

• Business Level Japanese Required

◆ Fully Remote Work

◆ Listed Company

◆ In-house Products/Services

◆ Great Welfare

◆ Annual salary: 10 million yen - 18 million yen

-------------【About the company】-------------

The company's philosophy is to create a society where people all over the world can freely utilize "wisdom to live better = intellectual information" and to create a society where people can live happily.

It provides a wide range of services, including different portal sites and the Web-based cloud contract service.

-------------【 Job Description】-------------

Established in January 2026, the department is rapidly accelerating AI integration across multiple products.

The true value of AI/ML goes far beyond merely building a model; it depends heavily on how stably the system can operate while maintaining high accuracy and undergoing continuous improvement. Particularly with the widespread adoption of LLMs, managing the increasing complexity of evaluation methodologies and data freshness has become a critical challenge.

The company is seeking a Software Engineer who will work closely alongside product teams to maximize concrete business outcomes by automating and advancing the entire ML lifecycle (development, evaluation, deployment, and monitoring).

■Responsibilities

Implementing the ML Lifecycle into Products

・Collaborative design and construction of inference infrastructure and deployment pipelines for AI/ML features alongside individual product teams.

・Implementation of systems that support the continuous optimization of LLM applications (such as RAG) tailored directly to on-the-ground operational needs.

Developing Advanced Evaluation & Monitoring Infrastructure

・Automation of evaluation pipelines (e.g., LLM-as-a-judge frameworks) to handle increasingly complex AI outputs.

・Pipeline construction to enable the continuous accuracy evaluation of RAG architectures and search indexing functions.

・Establishment of monitoring and visualization frameworks for inference accuracy, latency, infrastructure costs, and data drift.

Delivering Enterprise-Wide AI Platform Components

・Operation of cross-product LLM orchestration and search infrastructure layers.

・Reduction of development lead times for both ML engineers and product developers through the standardization of development environments.

Collaborating with SRE and Infrastructure Teams

・Construction of secure, scalable AI/ML and search systems aligned with company-wide infrastructure policies.

・Execution of cost optimization strategies and technical performance tuning.

Development Environment

Languages and Frameworks

Backend: Go

Machine Learning: Python / scikit-learn, etc.

LLM: LangChain, LangGraph, Langfuse

Technical Infrastructure

Infrastructure: AWS / Google Cloud

Databases: Aurora / BigQuery

AI/Search: Bedrock / Gemini / SageMaker / Vertex AI / OpenSearch

Project Management and Source Code Management

Project Management: JIRA

Source Code Management: GitHub

CI/CD: GitHub Actions

Information Sharing and Development Support

Information Sharing: Slack, Google Workspace, esa.io

Development Support: Claude Code, Cursor, Devin, etc.

Position Highlights

Directly Sustaining Scaled Stability and Rapid Innovation

・You will experience the tangible impact of seeing the systems you build directly support both the stable operation of large-scale products—such as CloudSign—and the rapid, continuous optimization of cutting-edge capabilities like the Legal Brain Agent.

Tackling Uncharted Challenges in Modern AI

・In addition to conventional machine learning pipelines, you can challenge yourself with ambiguous, evolving domains. This includes navigating the complexities of LLM-specific evaluation frameworks and automating intricate data workflows, such as dynamic index updates for RAG architectures.

Driving Enterprise-Wide Technical Leverage

・You will engage in high-impact organizational work by abstracting localized product bottlenecks into foundational, platform-level components, effectively multiplying development velocity across the entire company.

■ About the Services & Business Unit

AI Infrastructure

This core technology integrates generative AI with the company's proprietary database. This database enhances a vast collection of laws, regulations, and guidelines by compounding it with immense volumes of legal data accumulated by the group—including past judicial precedents, professional legal literature, legal consultation records, and insights provided directly by attorneys. By graphing the complex interrelationships between these disparate datasets, the company has built a highly specialized knowledge base. Leveraging this platform enables the realization of legal services that offer unprecedented convenience and utility.

■About the Department

Data Division, AI Technology Development Department

The department’s mission is to empower individual business units across the company to focus entirely on creating customer value at maximum speed, eliminating the need for them to spend excessive time on AI trial-and-error.

To achieve this, the department provides "embedded technical support"—deeply integrating with individual product development teams to co-execute AI/ML experimentation, accuracy evaluation, and continuous optimization.

Furthermore, by building foundational AI infrastructure to resolve common bottlenecks identified through these embedded initiatives, the department scales successful outcomes horizontally across multiple products, ultimately maximizing the product competitiveness of the entire company.

-------------【 Requirements】-------------

Required

Experience building and operating infrastructure and services using public clouds (AWS, GCP, Azure)

Basic knowledge of the machine learning system lifecycle (training, evaluation, inference)

Experience automating development processes using CI/CD tools (GitHub Actions, CircleCI, etc.)

Practical experience with container technologies such as Docker and Kubernetes

Experience in software development using Python, Go, or similar languages

Preferred

Experience in developing and operating data platforms, ML platforms, and similar systems

Experience operating LLM applications and building evaluation pipelines

Experience managing infrastructure using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation

Experience monitoring service KPIs and implementing distributed tracing (e.g., Datadog, OpenTelemetry)

Experience designing and operating microservices capable of handling large-scale traffic

Ideal Applicants

Empathy for the challenges faced on the front lines

Enjoys identifying the challenges faced by product teams and solving them through technology

Driven by a passion for “automation”

Avoids manual errors and delays, and is committed to thoroughly automating processes

A cross-functional mindset

Possesses infrastructure knowledge while also having a deep interest in the domain of machine learning engineers (such as model evaluation and data processing), and can serve as a bridge between teams

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【Welfare】

■Housing Allowance: An allowance of up to ¥30,000 is provided to employees whose home is located less than 2km (straight-line distance) from the company office.

■Employee Stock Ownership Plan

■Defined Contribution (DC) Pension Plan

■Club Activity Subsidy System: Various clubs are organized internally to deepen interaction among employees. The company provides a partial subsidy for activity expenses when employees belong to a circle and actively participate.

■Regular Social Gatherings: To foster employee interaction, the company hosts gatherings such as a Beer Bash (held internally on a Friday once a month) and General Employee Meetings (held at an external venue with all employees present).

■Shuffle Lunch: To deepen employee interaction, once a month, the company subsidizes lunch for groups of 4-5 randomly selected employees. The company provides a subsidy of ¥1,500 per person for the lunch expense.

■Resort Support System: A system allowing employees to stay at a private, membership-based resort hotel, with access to diverse facilities such as hot springs, sports, and restaurants.

■Internal Commendation System: A system used during General Employee Meetings to recognize employees who embody excellence and serve as role models (e.g., MVP awards).

■Real Estate Brokerage Fee Discount: Employees relocating receive a discount on brokerage fees if they use the real estate agency with which the company has a corporate contract.

■Babysitter Expense Subsidy: Expenses are subsidized for employees who need to use a babysitter to attend internal events, starting with the regular social gatherings.

■Book Purchase System: The company covers the cost of purchasing books, provided certain conditions are met.

■Side Jobs (Fukugyo) Allowed: Side jobs are permitted upon approval.

■PC Provision: The standard PC provided is a MacBook Air. (MacBook Pro is provided to creative roles such as engineers and designers.)

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