diff --git a/src/anemoi/training/diagnostics/callbacks/__init__.py b/src/anemoi/training/diagnostics/callbacks/__init__.py
index f6a7b894..f3597843 100644
--- a/src/anemoi/training/diagnostics/callbacks/__init__.py
+++ b/src/anemoi/training/diagnostics/callbacks/__init__.py
@@ -56,8 +56,8 @@ def nestedget(conf: DictConfig, key: str, default: Any) -> Any:
 ]
 
 
-def _get_checkpoint_callback(config: DictConfig) -> list[AnemoiCheckpoint] | None:
-    """Get checkpointing callback."""
+def _get_checkpoint_callback(config: DictConfig) -> list[AnemoiCheckpoint]:
+    """Get checkpointing callbacks."""
     if not config.diagnostics.get("enable_checkpointing", True):
         return []
 
@@ -89,6 +89,7 @@ def _get_checkpoint_callback(config: DictConfig) -> list[AnemoiCheckpoint] | Non
             n_saved,
         )
 
+    checkpoint_callbacks = []
     if not config.diagnostics.profiler:
         for save_key, (
             name,
@@ -97,29 +98,27 @@ def _get_checkpoint_callback(config: DictConfig) -> list[AnemoiCheckpoint] | Non
         ) in ckpt_frequency_save_dict.items():
             if save_frequency is not None:
                 LOGGER.debug("Checkpoint callback at %s = %s ...", save_key, save_frequency)
-                return (
+                checkpoint_callbacks.append(
                     # save_top_k: the save_top_k flag can either save the best or the last k checkpoints
                     # depending on the monitor flag on ModelCheckpoint.
                     # See https://lightning.ai/docs/pytorch/stable/common/checkpointing_intermediate.html for reference
-                    [
-                        AnemoiCheckpoint(
-                            config=config,
-                            filename=name,
-                            save_last=True,
-                            **{save_key: save_frequency},
-                            # if save_top_k == k, last k models saved; if save_top_k == -1, all models are saved
-                            save_top_k=save_n_models,
-                            monitor="step",
-                            mode="max",
-                            **checkpoint_settings,
-                        ),
-                    ]
+                    AnemoiCheckpoint(
+                        config=config,
+                        filename=name,
+                        save_last=True,
+                        **{save_key: save_frequency},
+                        # if save_top_k == k, last k models saved; if save_top_k == -1, all models are saved
+                        save_top_k=save_n_models,
+                        monitor="step",
+                        mode="max",
+                        **checkpoint_settings,
+                    ),
                 )
             LOGGER.debug("Not setting up a checkpoint callback with %s", save_key)
     else:
         # the tensorboard logger + pytorch profiler cause pickling errors when writing checkpoints
         LOGGER.warning("Profiling is enabled - will not write any training or inference model checkpoints!")
-    return None
+    return checkpoint_callbacks
 
 
 def _get_config_enabled_callbacks(config: DictConfig) -> list[Callback]:
@@ -180,9 +179,7 @@ def get_callbacks(config: DictConfig) -> list[Callback]:
     trainer_callbacks: list[Callback] = []
 
     # Get Checkpoint callback
-    checkpoint_callback = _get_checkpoint_callback(config)
-    if checkpoint_callback is not None:
-        trainer_callbacks.extend(checkpoint_callback)
+    trainer_callbacks.extend(_get_checkpoint_callback(config))
 
     # Base callbacks
     trainer_callbacks.extend(