diff --git a/exla/lib/exla/backend.ex b/exla/lib/exla/backend.ex
index 4f394d4c6a..973c1b7353 100644
--- a/exla/lib/exla/backend.ex
+++ b/exla/lib/exla/backend.ex
@@ -261,7 +261,6 @@ defmodule EXLA.Backend do
       {:dot, [:left, :c1, :b1, :right, :c2, :b2], [:left, :right]},
       {:clip, [:tensor, :min, :max], [:tensor, :min, :max]},
       {:take, [:tensor, :indices, :axis], [:tensor, :indices]},
-      {:take_along_axis, [:tensor, :indices, :axis], [:tensor, :indices]},
       {:gather, [:input, :indices, :opts], [:input, :indices]},
       {:select, [:pred, :on_true, :on_false], [:pred, :on_true, :on_false]},
       {:conv, [:tensor, :kernel, :opts], [:tensor, :kernel]},
diff --git a/exla/lib/exla/defn.ex b/exla/lib/exla/defn.ex
index d29e4bbcd4..e08a38135b 100644
--- a/exla/lib/exla/defn.ex
+++ b/exla/lib/exla/defn.ex
@@ -1608,44 +1608,6 @@ defmodule EXLA.Defn do
     )
   end
 
-  defp to_operator(:take_along_axis, [%mod{} = tensor, indices, axis], _ans, state) do
-    indices_shape = op_shape(indices)
-    indices_rank = tuple_size(indices_shape)
-
-    axes_range = 0..(indices_rank - 1)//1
-
-    index_vector_dim = indices_rank
-    slice_sizes = List.duplicate(1, indices_rank)
-    offset_dims = []
-    collapsed_slice_dims = Enum.to_list(axes_range)
-    start_index_map = Enum.to_list(axes_range)
-
-    indices_exla_shape = mod.get_shape(indices)
-
-    iotas =
-      Enum.map(axes_range, fn axis ->
-        mod.iota(state.builder, indices_exla_shape, axis)
-      end)
-
-    new_axis_shape = Tuple.append(indices_shape, 1)
-
-    indices =
-      iotas
-      |> List.replace_at(axis, indices)
-      |> Enum.map(&mod.reshape(&1, new_axis_shape))
-      |> mod.concatenate(indices_rank)
-
-    mod.gather(
-      tensor,
-      indices,
-      index_vector_dim,
-      slice_sizes,
-      offset_dims,
-      collapsed_slice_dims,
-      start_index_map
-    )
-  end
-
   defp to_operator(:gather, [%mod{} = tensor, indices, opts], _ans, _state) do
     axes = Keyword.fetch!(opts, :axes)
     tensor_shape = op_shape(tensor)