Machine Learning Engineer (Global Business)

**About the company:** VISASQ Meguro-ku, Tokyo VisasQ operates a global knowledge platform connecting organizations with more than 800,000 experts across 190 countries. Serving over 2,000 clients worldwide, the company helps businesses access first-hand insights through interviews, surveys, and advisory work. Read more **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 Requirements 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). Nice to haves While not specifically required, tell us if you have any of the following. 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). Compensation ¥7,000,000 ~ ¥15,000,000 annually. **Requirements:** 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). **Nice to have:** While not specifically required, tell us if you have any of the following. 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). **Compensation:** ¥7,000,000 ~ ¥15,000,000 annually.