Machine Learning Engineer（Global Business）／プラットフォーム企業の求人

Responsibilities
- Collaborating with global English-speaking engineering team in Japan, US and Europe

- Designing, implementing, and validating machine learning models and AI agents across multiple providers (OpenAI, Anthropic, Google) to ensure cost-optimization and system resilience

- Promoting CI/CD pipelines, training, model monitoring, and version control based on MLOps best practices

- Optimizing machine learning model performance and scalability

- Fine-tuning large language models (LLMs) and designing/implementing training

- Stay current with the latest trends in machine learning, MLOps, and LLMs; researching and proposing new technologies and methodologies


Highlights of the Role
- End-to-End Ownership: Work closely with the business side throughout the entire lifecycle, from initial problem-solving and specification design to release and continuous iteration.

- Proactive Innovation: We don’t just hand you tickets; engineers are expected and encouraged to propose their own ideas and shape the product roadmap from the planning stage.

- Global Mobility: Depending on your performance and preferences, there are opportunities to work at our New York development hub to accelerate your global career.


Technical Environment
- Languages: Python, SQL
- AI/ML & Orchestration: Azure OpenAI, Gemini, Anthropic, LangChain, LangGraph, Scikit-learn, Transformers
- Backend & Web: FastAPI, Streamlit, SQLAlchemy
- Data & Search: Azure Cognitive Search, Elasticsearch, Redis, CosmosDB, PostgreSQL
- Infrastructure & DevOps: Docker, Kubernetes, Azure Functions, Azure DevOps

Required Qualifications
- Business-level English proficiency for cross-border collaboration with US and European teams.

- Production ML & Backend Experience: Deep hands-on experience building production-grade backend services and ML pipelines.
- Mid-level: 3+ years of experience; comfortable navigating large, multi-module Python codebases independently.
- Senior-level: 5+ years of experience; able to architect event-driven workflows, optimize system latency, and design hybrid search retrieval.

- Core Technical Competency:
- Experience with LLM orchestration (structured outputs, tool use, multi-step agent workflows).
- Practical experience with tabular ML pipelines (feature engineering, ranking models/LTR, model validation).
-Familiarity with modern DevOps/MLOps (Docker, pytest/mocking, CI/CD pipelines).


Preferred Qualifications
- Cloud Infrastructure: Experience deploying and scaling services in public cloud environments
- Product Growth: Track record of iteratively scaling products in a fast-paced, cross-functional startup or scale-up environment.
- Language: Business-level or conversational Japanese Proficiency (to seamlessly interface with local product and business teams).

日本最大級のナレッジプラットフォーム運営企業