Define and drive the technical roadmap for vertical ranking, building scalable, low-latency ranking systems and multi-task learning pipelines on massive user data.
Partner with Product and Business to translate goals into technical requirements and KPIs; design high-impact ranking strategies; integrate Foundation Models and LLMs.
Lead architectural decisions for the recommendation stack, ensuring alignment with long-term product vision and cross-surface needs.
Mentor engineers, lead design reviews, and establish MLOps practices for end-to-end model lifecycle management.
Drive advanced ML innovations (LLMs, reinforcement learning, graph neural networks) across Search, Ads, and Personalization Feeds.
Oversee experimentation: complex A/B testing and offline-online correlation analyses to inform data-driven decisions.
技術スタック
必須スキル
5–10+ years (Staff) or 10+ years (Senior Staff) of applied ML experience with large-scale recommendation/ranking or computational advertising.
Deep learning architectures (Transformers, MoE, Embeddings) and production deployment at scale.
End-to-end ML pipeline design: data ingestion, feature engineering, online inference, monitoring.
Strong leadership, communication, and stakeholder management.
歓迎スキル（該当する場合）
Publications or open-source contributions; ML community recognition.
Infrastructure expertise (Spark, Ray) and high-performance serving infrastructure.
Japanese language proficiency (professional working).
キャリア成長観点
Shape the growth engine by defining the vertical ranking roadmap and translating business goals into measurable ML impact on engagement and monetization.
Lead adoption of state-of-the-art techniques (LLMs, RL, GNNs) and mature MLOps to scale impact; mentor and elevate engineers.
Work on scalable, low-latency systems at massive scale with cross-functional and global collaborators across product surfaces (For You, Follow, Search, Ads).
Foster a data-driven experimentation culture with robust online/offline evaluation to drive meaningful business outcomes.