Learning high-quality sentence embeddings from Natural Language Inference (NLI) data is often challenged by a critical signal conflict between discrete labels and the continuous spectrum of semantic similarity, as well as information loss from discarded neutral sentence pairs during training.To address this, we introduce Rank-Awareness and Angular Optimization Embeddings (RAOE), a framework that leverages the full NLI dataset (Entailment, Neutral, Contradiction) augmented with precomputed continuous similarity scores (S).RAOE employs a novel composite objective which features: (1) a Rank Margin objective that enforces rank consistency against S using an explicit margin, and (2) a Gated Angular objective that conditionally refines embedding geometry based on NLI label (L) and S score agreement.Extensive evaluations on STS tasks and the MTEB benchmark demonstrate RAOE's effectiveness.Our generalpurpose RAOE-S1 model (BERT-base) significantly outperforms strong baselines, achieving an average Spearman's correlation of 85.11 (vs.SimCSE's 81.57and AnglE's 82.43), and shows consistent improvements on MTEB.Further STS-specialized fine-tuning (RAOE-S2) establishes new state-of-the-art performance on STS (88.17 with BERT-base).These results confirm RAOE's ability to efficiently learn robust and nuanced sentence representations through the synergy of rankawareness and conditional angular constraints.