Semantic ID (SID) serves as a key foundation for various generative recommendation systems. To enable the unified generation of SIDs across heterogeneous multimodal features, we propose SEALNet (SEmantic ALignment NETwork), a general framework that learns to align and integrate the semantics of diverse input features. Building upon this framework, we develop a family of RankIDs that are task-specific SIDs tailored for large-scale recommendation systems. Two representative implementations are presented: RankID-Graph, which enriches a content understanding feature with creator-level co-engagement information, and RankID-LLM, which integrates the semantic information from multiple features into a single SID.