diff --git a/keras_hub/src/layers/modeling/position_embedding.py b/keras_hub/src/layers/modeling/position_embedding.py
index afa0c8f679..8e77207baf 100644
--- a/keras_hub/src/layers/modeling/position_embedding.py
+++ b/keras_hub/src/layers/modeling/position_embedding.py
@@ -31,6 +31,10 @@ class PositionEmbedding(keras.layers.Layer):
         start_index: An integer or integer tensor. The starting position to
             compute the position embedding from. This is useful during cached
             decoding, where each position is predicted separately in a loop.
+        hierarchical_alpha:Hyperparameters of hierarchical positional encoding.
+            Hierarchical positional encoding allows models such as BERT and ViT
+            to be seamlessly expanded to a length of sequence_length**2.
+            The range of this hyperparameter is (0,1) and not equal to 0.5
 
     Example:
 
@@ -61,6 +65,7 @@ def __init__(
         self,
         sequence_length,
         initializer="glorot_uniform",
+        hierarchical_alpha=0.4,
         **kwargs,
     ):
         super().__init__(**kwargs)
@@ -69,13 +74,23 @@ def __init__(
                 "`sequence_length` must be an Integer, received `None`."
             )
         self.sequence_length = int(sequence_length)
+        self.hierarchical_alpha = hierarchical_alpha
         self.initializer = keras.initializers.get(initializer)
+        if (
+            hierarchical_alpha <= 0
+            or hierarchical_alpha >= 1
+            or hierarchical_alpha == 0.5
+        ):
+            raise ValueError(
+                "`hierarchical_alpha` must be in (0,1) and not equal to 0.5."
+            )
 
     def get_config(self):
         config = super().get_config()
         config.update(
             {
                 "sequence_length": self.sequence_length,
+                "hierarchical_alpha": self.hierarchical_alpha,
                 "initializer": keras.initializers.serialize(self.initializer),
             }
         )
@@ -98,11 +113,32 @@ def call(self, inputs, start_index=0):
         # trim to match the length of the input sequence, which might be less
         # than the sequence_length of the layer.
         position_embeddings = ops.convert_to_tensor(self.position_embeddings)
-        position_embeddings = ops.slice(
-            position_embeddings,
-            (start_index, 0),
-            (sequence_length, feature_length),
-        )
+        if sequence_length < self.sequence_length:
+            position_embeddings = ops.slice(
+                position_embeddings,
+                (start_index, 0),
+                (sequence_length, feature_length),
+            )
+
+        else:
+            embeddings = (
+                position_embeddings
+                - self.hierarchical_alpha * position_embeddings[:1]
+            )
+            embeddings = embeddings / (1 - self.hierarchical_alpha)
+            position_ids = (
+                ops.arange(sequence_length, dtype="int32") + start_index
+            )
+            embeddings_x = ops.take(
+                embeddings, position_ids // self.sequence_length, axis=0
+            )
+            embeddings_y = ops.take(
+                embeddings, position_ids % self.sequence_length, axis=0
+            )
+            position_embeddings = (
+                self.hierarchical_alpha * embeddings_x
+                + (1 - self.hierarchical_alpha) * embeddings_y
+            )
         return ops.broadcast_to(position_embeddings, shape)
 
     def compute_output_shape(self, input_shape):
diff --git a/keras_hub/src/layers/modeling/position_embedding_test.py b/keras_hub/src/layers/modeling/position_embedding_test.py
index d6e577c66e..a086942e29 100644
--- a/keras_hub/src/layers/modeling/position_embedding_test.py
+++ b/keras_hub/src/layers/modeling/position_embedding_test.py
@@ -37,6 +37,21 @@ def test_layer_behaviors_4d(self):
             expected_num_trainable_weights=1,
         )
 
+    def test_layer_behaviors_hierarchical(self):
+        self.run_layer_test(
+            cls=PositionEmbedding,
+            init_kwargs={
+                "sequence_length": 4,
+            },
+            input_data=random.uniform(shape=(4, 16, 30)),
+            expected_output_shape=(4, 16, 30),
+            expected_num_trainable_weights=1,
+        )
+        layer = PositionEmbedding(sequence_length=8)
+        outputs1 = layer(random.uniform(shape=(2, 4, 30)))
+        outputs2 = layer(random.uniform(shape=(2, 16, 30)))
+        self.assertAllClose(outputs1, outputs2[:, :4], rtol=1e-4, atol=1e-7)
+
     def test_float16_dtype(self):
         # Create a 3-dimensional input (the first dimension is implicit).
         sequence_length = 21