diff --git a/merlin/models/tf/losses/pairwise.py b/merlin/models/tf/losses/pairwise.py
index 03c795a063..97045a4cec 100644
--- a/merlin/models/tf/losses/pairwise.py
+++ b/merlin/models/tf/losses/pairwise.py
@@ -72,6 +72,10 @@ def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
         tf.Tensor
             Loss per example
         """
+        tf.assert_equal(tf.rank(y_true), 2, f"Targets must be 2-D tensor (got {y_true.shape})")
+
+        tf.assert_equal(tf.rank(y_pred), 2, f"Predictions must be 2-D tensor (got {y_pred.shape})")
+
         (
             positives_scores,
             negatives_scores,
diff --git a/merlin/models/tf/outputs/contrastive.py b/merlin/models/tf/outputs/contrastive.py
index 9f712d11d0..f5eb0497f6 100644
--- a/merlin/models/tf/outputs/contrastive.py
+++ b/merlin/models/tf/outputs/contrastive.py
@@ -226,7 +226,6 @@ def outputs(
         # To ensure that the output is always fp32, avoiding numerical
         # instabilities with mixed_float16 policy
         outputs = tf.cast(outputs, tf.float32)
-        outputs = tf.squeeze(outputs)
 
         targets = tf.concat(
             [
diff --git a/tests/unit/tf/outputs/test_contrastive.py b/tests/unit/tf/outputs/test_contrastive.py
index 90b8b077b3..04c92863dc 100644
--- a/tests/unit/tf/outputs/test_contrastive.py
+++ b/tests/unit/tf/outputs/test_contrastive.py
@@ -190,6 +190,21 @@ def test_contrastive_only_positive_when_not_training(ecommerce_data: Dataset):
     )
 
 
+@pytest.mark.parametrize("run_eagerly", [True, False])
+def test_contrastive_output_with_pairwise_loss(ecommerce_data: Dataset, run_eagerly):
+    model = mm.RetrievalModelV2(
+        query=mm.Encoder(ecommerce_data.schema.select_by_tag(Tags.USER), mm.MLPBlock([2])),
+        candidate=mm.Encoder(ecommerce_data.schema.select_by_tag(Tags.ITEM), mm.MLPBlock([2])),
+        output=mm.ContrastiveOutput(
+            ecommerce_data.schema.select_by_tag(Tags.ITEM_ID),
+            negative_samplers="in-batch",
+            candidate_name="item",
+        ),
+    )
+    model.compile(run_eagerly=run_eagerly, loss="bpr-max")
+    _ = model.fit(ecommerce_data, batch_size=50, epochs=1)
+
+
 def _retrieval_inputs_(batch_size):
     users_embeddings = tf.random.uniform(shape=(batch_size, 5), dtype=tf.float32)
     items_embeddings = tf.random.uniform(shape=(batch_size, 5), dtype=tf.float32)