diff --git a/jax/_src/pallas/mosaic_gpu/lowering.py b/jax/_src/pallas/mosaic_gpu/lowering.py
index dc2d63f0b6ec..8e5fb236928e 100644
--- a/jax/_src/pallas/mosaic_gpu/lowering.py
+++ b/jax/_src/pallas/mosaic_gpu/lowering.py
@@ -1012,19 +1012,23 @@ def _handle_indexing(
   ]
   if not indexer_idxs:
     return ref, transforms
-  if len(indexer_idxs) > 1:
-    raise NotImplementedError("Only one level of indexing supported.")
-  [indexer_idx] = indexer_idxs
-  indexer = cast(indexing.NDIndexer, transforms[indexer_idx])
-  if indexer.int_indexer_shape:
-    raise NotImplementedError("int_indexer_shape non-empty")
-  indices = _ndindexer_indices(indexer)
-  new_transforms_rev = []
-  for t in reversed(transforms[:indexer_idx]):
-    indices, new_t = t.untransform_index(indices)
-    new_transforms_rev.append(new_t)
-  new_transforms = [*reversed(new_transforms_rev), *transforms[indexer_idx + 1:]]
-  return mgpu.memref_slice(ref, indices), new_transforms
+  sliced_ref = ref
+  new_transforms = []
+  for t in transforms:
+    if not isinstance(t, indexing.NDIndexer):
+      new_transforms.append(t)
+      continue
+    indexer = cast(indexing.NDIndexer, t)
+    if indexer.int_indexer_shape:
+      raise NotImplementedError("int_indexer_shape non-empty")
+    indices = _ndindexer_indices(indexer)
+    new_transforms_rev = []
+    for t in reversed(new_transforms):
+      indices, new_t = t.untransform_index(indices)
+      new_transforms_rev.append(new_t)
+    sliced_ref = mgpu.memref_slice(sliced_ref, indices)
+    new_transforms = list(reversed(new_transforms_rev))
+  return sliced_ref, new_transforms
 
 
 def _ndindexer_indices(indexer: indexing.NDIndexer) -> tuple[gpu_core.Index, ...]:
diff --git a/tests/pallas/mosaic_gpu_test.py b/tests/pallas/mosaic_gpu_test.py
index a9d5361e7c10..b57cbd3d4f03 100644
--- a/tests/pallas/mosaic_gpu_test.py
+++ b/tests/pallas/mosaic_gpu_test.py
@@ -301,6 +301,50 @@ def kernel(x_ref_gmem, o_ref, scratch_ref, barrier_ref):
     x = jnp.arange(256).astype(jnp.float32)
     np.testing.assert_array_equal(kernel(x)[indexer], x[indexer] + 1.0)
 
+  def test_ref_with_multiple_indexers(self):
+    x = jax.random.uniform(jax.random.key(0), (2, 64, 64))
+    @functools.partial(
+        pl.pallas_call,
+        out_shape=jax.ShapeDtypeStruct([64, 64], jnp.float32),
+        in_specs=(pl.BlockSpec(memory_space=plgpu.GMEM),),
+        scratch_shapes=[
+            plgpu.SMEM(x.shape, jnp.float32),
+            plgpu.Barrier(num_arrivals=1),
+        ],
+    )
+    def extract_x0(x_ref_gmem, o_ref, scratch_ref, barrier_ref):
+      plgpu.copy_gmem_to_smem(x_ref_gmem, scratch_ref, barrier_ref)
+      plgpu.barrier_wait(barrier_ref)
+      x_sliced = scratch_ref.at[0, :, :]  # shape=(64, 64)
+      o_ref[pl.ds(0, 32), :] = x_sliced[pl.ds(0, 32), :]
+      o_ref[pl.ds(32, 32), :] = x_sliced[pl.ds(32, 32), :]
+    np.testing.assert_array_equal(extract_x0(x), x[0])
+
+  def test_smem_multiple_indexers_with_transforms(self):
+    x = jnp.arange(512 * 512).reshape(512, 512)
+    @functools.partial(
+        pl.pallas_call,
+        grid=(4, 4),
+        out_shape=jax.ShapeDtypeStruct((256, 128), jnp.int32),
+        in_specs=(plgpu.GPUBlockSpec(
+            block_shape=(128, 128),
+            index_map=lambda i, j: (i, j),
+            memory_space=plgpu.SMEM,
+            transforms=(plgpu.TilingTransform((64, 32)),
+                        plgpu.SwizzleTransform(128))),),
+        out_specs=(plgpu.GPUBlockSpec(
+            block_shape=(64, 32),
+            index_map=lambda i, j: (i, j),
+            memory_space=plgpu.SMEM,)),
+    )
+    def kernel(x_ref, o_ref):
+      x_sliced = x_ref.at[0:64, 32:96].at[:, 0:32]  # get x_ref[0:64, 32:64]
+      o_ref[...] = x_sliced[...]
+    ref = jnp.concatenate([x[blk:blk+64, :] for blk in range(0, 512, 128)])
+    ref = jnp.concatenate(
+        [ref[:, blk+32:blk+64] for blk in range(0, 512, 128)], axis=1)
+    np.testing.assert_array_equal(kernel(x), ref)
+
   @parameterized.product(indexer=[0, 1, 2, 3])
   def test_copy_gmem_to_smem_with_indexed_barrier(self, indexer):
     @functools.partial(