diff --git a/grain/_src/python/dataset/transformations/map.py b/grain/_src/python/dataset/transformations/map.py
index 18e59bc3..bd1ba722 100644
--- a/grain/_src/python/dataset/transformations/map.py
+++ b/grain/_src/python/dataset/transformations/map.py
@@ -20,6 +20,7 @@
 from absl import logging
 from grain._src.core import transforms
 from grain._src.python.dataset import dataset
+from grain._src.python.dataset.transformations import prefetch
 import numpy as np
 
 
@@ -233,7 +234,14 @@ def __next__(self):
     with self._stats.record_self_time():
       if element is not None:
         if self._seed is not None:
-          _reset_rng_state(self._rng, op_seed=0, index=self._index_for_rng)
+          # Shift index for the current worker process in case of multiprocess
+          # execution. The actual index value doesn't matter as long as it is
+          # unique for each process.
+          index_for_rng = (
+              prefetch.worker_process_index
+              + self._index_for_rng * prefetch.worker_process_count
+          )
+          _reset_rng_state(self._rng, op_seed=0, index=index_for_rng)
           element = self._map_fn(element, self._rng)
         else:
           element = self._map_fn(element)
diff --git a/grain/_src/python/dataset/transformations/prefetch.py b/grain/_src/python/dataset/transformations/prefetch.py
index 5353451a..2bd5fc2e 100644
--- a/grain/_src/python/dataset/transformations/prefetch.py
+++ b/grain/_src/python/dataset/transformations/prefetch.py
@@ -41,6 +41,14 @@
 
 T = TypeVar("T")
 
+# Index of the current worker process and total number of processes. If used
+# before multiprocess prefetch, must only be used during or after iterator
+# initialization.
+# TODO: Introduce context shared by all iterators and put these
+# variables there.
+worker_process_index = 0
+worker_process_count = 1
+
 
 @typing.runtime_checkable
 class SupportsInPlaceSlicing(Protocol):
@@ -374,12 +382,16 @@ def __init__(
   def __call__(
       self, *, worker_index: int, worker_count: int
   ) -> Iterator[tuple[T, Optional[dict[str, Any]]]]:
-    # Recover from the last recorded state for the given worker.
-    worker_state = self._state[_WORKERS_STATE][str(worker_index)]
+    global worker_process_index, worker_process_count
+    worker_process_index = worker_index
+    worker_process_count = worker_count
     if worker_count > 1:
       _set_slice(self._ds, slice(worker_index, None, worker_count))
-    it = iter(self._ds)
-    it.set_state(worker_state)  # pytype: disable=attribute-error
+    it = self._ds.__iter__()
+    # Recover from the last recorded state for the given worker.
+    worker_state = self._state[_WORKERS_STATE][str(worker_index)]
+    if worker_state is not None:
+      it.set_state(worker_state)
     # Skip the required number of iterations after the last recorded state.
     for _ in range(self._state[_ITERATIONS_TO_SKIP][str(worker_index)]):
       _ = next(it)
@@ -432,13 +444,12 @@ def __init__(
     # Create initial state. We record state of each worker periodically together
     # with the number of iterations without the recorded state and index of the
     # last worker.
-    workers_state: dict[str, Any] = {}
-    iterations_to_skip: dict[str, int] = {}
-    for i in range(multiprocessing_options.num_workers):
-      workers_state[str(i)] = iter(
-          self._iter_parent
-      ).get_state()  # pytype: disable=attribute-error
-      iterations_to_skip[str(i)] = 0
+    iterations_to_skip: dict[str, int] = {
+        str(i): 0 for i in range(multiprocessing_options.num_workers)
+    }
+    workers_state: dict[str, Any] = {
+        str(i): None for i in range(multiprocessing_options.num_workers)
+    }
 
     self._state: dict[str, dict[str, Any] | int] = {
         _WORKERS_STATE: workers_state,
@@ -483,7 +494,16 @@ def set_state(self, state: dict[str, dict[str, Any] | int]) -> None:
     self._iterator = None
 
   def get_state(self) -> dict[str, Any]:
-    return copy.deepcopy(self._state)
+    result = copy.deepcopy(self._state)
+    workers_state: dict[str, Any] = result[_WORKERS_STATE]  # pytype: disable=annotation-type-mismatch
+    parent_state = None
+    for worker_index, worker_state in workers_state.items():
+      # Create initial state from the parent iterator. This is to make sure the
+      # spec of the produced iterator does not change.
+      if worker_state is None:
+        parent_state = parent_state or self._iter_parent.__iter__().get_state()
+        workers_state[worker_index] = copy.deepcopy(parent_state)
+    return result
 
   def _ensure_iterator_initialized(self) -> None:
     if self._iterator is None:
diff --git a/grain/_src/python/dataset/transformations/prefetch_test.py b/grain/_src/python/dataset/transformations/prefetch_test.py
index 792e7b8b..db76c0a7 100644
--- a/grain/_src/python/dataset/transformations/prefetch_test.py
+++ b/grain/_src/python/dataset/transformations/prefetch_test.py
@@ -561,6 +561,19 @@ def map(self, features):
     # buffers.
     time.sleep(30)
 
+  def test_prefetch_with_random_map(self):
+    ds = dataset.MapDataset.source([0]).repeat(100).to_iter_dataset()
+    ds = ds.random_map(lambda x, rng: x + rng.integers(sys.maxsize), seed=42)
+    ds = prefetch.MultiprocessPrefetchIterDataset(
+        ds,
+        options.MultiprocessingOptions(num_workers=5),
+    )
+    # Make sure that sliced datasets on workers are seeded differently and thus
+    # produce different random elements.
+    elements = list(ds)
+    distinct_elements = set(elements)
+    self.assertLen(distinct_elements, len(elements))
+
 
 class ThreadPrefetchIterDatasetTest(parameterized.TestCase):