diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index d422fb1be9fdf..51b4c4f297b07 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -789,6 +789,7 @@ Plotting
 Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 - Bug in :meth:`.DataFrameGroupBy.idxmin`, :meth:`.SeriesGroupBy.idxmin`, :meth:`.DataFrameGroupBy.idxmax`, :meth:`.SeriesGroupBy.idxmax` returns wrong dtype when used on an empty DataFrameGroupBy or SeriesGroupBy (:issue:`51423`)
+- Bug in :meth:`DataFrame.groupby.rank` on nullable datatypes when passing ``na_option="bottom"`` or ``na_option="top"`` (:issue:`54206`)
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` in incorrectly allowing non-fixed ``freq`` when resampling on a :class:`TimedeltaIndex` (:issue:`51896`)
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` losing time zone when resampling empty data (:issue:`53664`)
 - Bug in :meth:`DataFrame.resample` and :meth:`Series.resample` where ``origin`` has no effect in resample when values are outside of axis  (:issue:`53662`)
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 2cf28c28427ab..fdcbe67bbc371 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -1487,6 +1487,9 @@ def _groupby_op(
         else:
             result_mask = np.zeros(ngroups, dtype=bool)
 
+        if how == "rank" and kwargs.get("na_option") in ["top", "bottom"]:
+            result_mask[:] = False
+
         res_values = op._cython_op_ndim_compat(
             self._data,
             min_count=min_count,
diff --git a/pandas/tests/groupby/test_rank.py b/pandas/tests/groupby/test_rank.py
index 5d85a0783e024..a3b7da3fa836c 100644
--- a/pandas/tests/groupby/test_rank.py
+++ b/pandas/tests/groupby/test_rank.py
@@ -710,3 +710,12 @@ def test_rank_categorical():
 
     expected = df.astype(object).groupby("col1").rank()
     tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.parametrize("na_option", ["top", "bottom"])
+def test_groupby_op_with_nullables(na_option):
+    # GH 54206
+    df = DataFrame({"x": [None]}, dtype="Float64")
+    result = df.groupby("x", dropna=False)["x"].rank(method="min", na_option=na_option)
+    expected = Series([1.0], dtype="Float64", name=result.name)
+    tm.assert_series_equal(result, expected)