Lead a team of engineers focused on developing advanced agentic AI and recommendation systems that power next-generation digital marketing solutions. This role sits at the intersection of machine learning, large language models, and enterprise SaaS, delivering intelligent, autonomous decisioning capabilities integrated with a unified data platform.
Key Responsibilities
- Guide engineering teams and collaborate with ML scientists to build and deploy production-grade agent architectures, including planning loops, tool integration, memory systems, and safety controls.
- Translate product vision into technical roadmaps, defining milestones and ensuring consistent, high-quality delivery.
- Champion engineering best practices: code quality, testing rigor, system observability, and incident management.
- Design evaluation frameworks combining offline benchmarks, live experiments, and human-in-the-loop validation to measure agent performance.
- Ensure seamless integration with a centralized data platform, upholding standards for data reliability, privacy, and feature availability.
- Work closely with Product, Design, Data Science, and Go-to-Market teams to align technical development with business outcomes.
- Connect engineering efforts directly to measurable impact—revenue, customer value, and retention.
- Foster a culture of autonomy, empowering both your team and the systems they build to operate with independence and accountability.
- Manage rapid experimentation cycles while maintaining security, compliance, and enterprise readiness.
Qualifications
Candidates should have at least eight years of software engineering experience, including four years in leadership roles. A background in computer science or equivalent practical experience is required. Proven success delivering reliable, secure, and observable enterprise systems is essential. Experience managing engineers and collaborating with ML teams is expected, along with fluency in Agile delivery.
Preferred Experience
- Production experience with LLM-powered agents, including retrieval-augmented generation (RAG), tool use, and orchestration.
- Hands-on work with ML systems in recommendation, ranking, forecasting, or customer lifetime value modeling.
- Familiarity with data platforms, feature stores, event pipelines, and data governance practices.
- Experience designing A/B tests and interpreting causal metrics to guide development.
- Knowledge of marketing automation or CRM domains such as customer segmentation, omnichannel messaging, and campaign operations.
- Leadership of hybrid engineering teams with both local and remote contributors.
Work Environment
This position is based in Tokyo, Japan, and requires relocation. The team operates in a fast-moving, feedback-driven environment that values efficiency, responsiveness, and continuous improvement. Innovation is rooted in rapid iteration, learning from setbacks, and making progress through clear measurement.
Technology Environment
The stack includes LLM orchestration, agent frameworks, retrieval systems, memory architectures, evaluation tooling, A/B testing, causal inference, data pipelines, identity resolution, feature stores, and enterprise data platforms built on lakehouse and warehouse infrastructure.
