diff --git a/keras_rs/src/layers/embedding/base_distributed_embedding.py b/keras_rs/src/layers/embedding/base_distributed_embedding.py
index 8d29cd7..c30993f 100644
--- a/keras_rs/src/layers/embedding/base_distributed_embedding.py
+++ b/keras_rs/src/layers/embedding/base_distributed_embedding.py
@@ -174,12 +174,9 @@ class DistributedEmbedding(keras.layers.Layer):
     supported on all backends and accelerators:
 
     - `keras.optimizers.Adagrad`
-    - `keras.optimizers.SGD`
-
-    The following are additionally available when using the TensorFlow backend:
-
     - `keras.optimizers.Adam`
     - `keras.optimizers.Ftrl`
+    - `keras.optimizers.SGD`
 
     Also, not all parameters of the optimizers are supported (e.g. the
     `nesterov` option of `SGD`). An error is raised when an unsupported
diff --git a/keras_rs/src/layers/embedding/jax/config_conversion.py b/keras_rs/src/layers/embedding/jax/config_conversion.py
index 25a3ef8..1d11908 100644
--- a/keras_rs/src/layers/embedding/jax/config_conversion.py
+++ b/keras_rs/src/layers/embedding/jax/config_conversion.py
@@ -229,18 +229,63 @@ def keras_to_jte_optimizer(
     # pylint: disable-next=protected-access
     learning_rate = keras_to_jte_learning_rate(optimizer._learning_rate)
 
-    # SGD or Adagrad
+    # Unsupported keras optimizer general options.
+    if optimizer.clipnorm is not None:
+        raise ValueError("Unsupported optimizer option `clipnorm`.")
+    if optimizer.global_clipnorm is not None:
+        raise ValueError("Unsupported optimizer option `global_clipnorm`.")
+    if optimizer.use_ema:
+        raise ValueError("Unsupported optimizer option `use_ema`.")
+    if optimizer.loss_scale_factor is not None:
+        raise ValueError("Unsupported optimizer option `loss_scale_factor`.")
+
+    # Supported optimizers.
     if isinstance(optimizer, keras.optimizers.SGD):
+        if getattr(optimizer, "nesterov", False):
+            raise ValueError("Unsupported optimizer option `nesterov`.")
+        if getattr(optimizer, "momentum", 0.0) != 0.0:
+            raise ValueError("Unsupported optimizer option `momentum`.")
         return embedding_spec.SGDOptimizerSpec(learning_rate=learning_rate)
     elif isinstance(optimizer, keras.optimizers.Adagrad):
+        if getattr(optimizer, "epsilon", 1e-7) != 1e-7:
+            raise ValueError("Unsupported optimizer option `epsilon`.")
         return embedding_spec.AdagradOptimizerSpec(
             learning_rate=learning_rate,
             initial_accumulator_value=optimizer.initial_accumulator_value,
         )
+    elif isinstance(optimizer, keras.optimizers.Adam):
+        if getattr(optimizer, "amsgrad", False):
+            raise ValueError("Unsupported optimizer option `amsgrad`.")
 
-    # Default to SGD for now, since other optimizers are still being created,
-    # and we don't want to fail.
-    return embedding_spec.SGDOptimizerSpec(learning_rate=learning_rate)
+        return embedding_spec.AdamOptimizerSpec(
+            learning_rate=learning_rate,
+            beta_1=optimizer.beta_1,
+            beta_2=optimizer.beta_2,
+            epsilon=optimizer.epsilon,
+        )
+    elif isinstance(optimizer, keras.optimizers.Ftrl):
+        if (
+            getattr(optimizer, "l2_shrinkage_regularization_strength", 0.0)
+            != 0.0
+        ):
+            raise ValueError(
+                "Unsupported optimizer option "
+                "`l2_shrinkage_regularization_strength`."
+            )
+
+        return embedding_spec.FTRLOptimizerSpec(
+            learning_rate=learning_rate,
+            learning_rate_power=optimizer.learning_rate_power,
+            l1_regularization_strength=optimizer.l1_regularization_strength,
+            l2_regularization_strength=optimizer.l2_regularization_strength,
+            beta=optimizer.beta,
+            initial_accumulator_value=optimizer.initial_accumulator_value,
+        )
+
+    raise ValueError(
+        f"Unsupported optimizer type {type(optimizer)}. Optimizer must be "
+        f"one of [Adagrad, Adam, Ftrl, SGD]."
+    )
 
 
 def jte_to_keras_optimizer(
@@ -262,8 +307,33 @@ def jte_to_keras_optimizer(
             learning_rate=learning_rate,
             initial_accumulator_value=optimizer.initial_accumulator_value,
         )
+    elif isinstance(optimizer, embedding_spec.AdamOptimizerSpec):
+        return keras.optimizers.Adam(
+            learning_rate=learning_rate,
+            beta_1=optimizer.beta_1,
+            beta_2=optimizer.beta_2,
+            epsilon=optimizer.epsilon,
+        )
+    elif isinstance(optimizer, embedding_spec.FTRLOptimizerSpec):
+        if getattr(optimizer, "initial_linear_value", 0.0) != 0.0:
+            raise ValueError(
+                "Unsupported optimizer option `initial_linear_value`."
+            )
+        if getattr(optimizer, "multiply_linear_by_learning_rate", False):
+            raise ValueError(
+                "Unsupported optimizer option "
+                "`multiply_linear_by_learning_rate`."
+            )
+        return keras.optimizers.Ftrl(
+            learning_rate=learning_rate,
+            learning_rate_power=optimizer.learning_rate_power,
+            initial_accumulator_value=optimizer.initial_accumulator_value,
+            l1_regularization_strength=optimizer.l1_regularization_strength,
+            l2_regularization_strength=optimizer.l2_regularization_strength,
+            beta=optimizer.beta,
+        )
 
-    raise ValueError(f"Unknown optimizer spec {optimizer}")
+    raise ValueError(f"Unknown optimizer spec {type(optimizer)}.")
 
 
 def _keras_to_jte_table_config(
diff --git a/keras_rs/src/layers/embedding/jax/config_conversion_test.py b/keras_rs/src/layers/embedding/jax/config_conversion_test.py
index 7b4c987..166e339 100644
--- a/keras_rs/src/layers/embedding/jax/config_conversion_test.py
+++ b/keras_rs/src/layers/embedding/jax/config_conversion_test.py
@@ -239,6 +239,13 @@ def test_initializer_conversion(
             ),
         ),
         ("Adagrad", lambda: keras.optimizers.Adagrad(learning_rate=0.02)),
+        ("Adam", lambda: keras.optimizers.Adam(learning_rate=0.03)),
+        (
+            "Ftrl",
+            lambda: keras.optimizers.Ftrl(
+                learning_rate=0.05,
+            ),
+        ),
         ("string", lambda: "adagrad"),
     )
     def test_optimizer_conversion(