diff --git a/CHANGES.rst b/CHANGES.rst
index 7794cb28..001bb430 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -9,6 +9,11 @@ Changes to API
 Bug Fixes
 ---------
 
+jump
+~~~~
+
+- Catch some additional warnings about all-NaN slices. [#258]
+
 ramp_fitting
 ~~~~~~~~~~~~
 
diff --git a/setup.py b/setup.py
index 592058e6..d5e47f0f 100644
--- a/setup.py
+++ b/setup.py
@@ -17,6 +17,8 @@
 # Setup C module macros
 define_macros = [("NUMPY", "1")]
 
+# importing these extension modules is tested in `.github/workflows/build.yml`; 
+# when adding new modules here, make sure to add them to the `test_command` entry there
 extensions = [
     Extension(
         "stcal.ramp_fitting.ols_cas22._ramp",
diff --git a/src/stcal/jump/jump.py b/src/stcal/jump/jump.py
index 47248831..f6e0e258 100644
--- a/src/stcal/jump/jump.py
+++ b/src/stcal/jump/jump.py
@@ -973,13 +973,13 @@ def find_faint_extended(
     all_ellipses = []
 
     first_diffs_masked = np.ma.masked_array(first_diffs, mask=np.isnan(first_diffs))
+    warnings.filterwarnings("ignore")
     if nints > minimum_sigclip_groups:
         mean, median, stddev = stats.sigma_clipped_stats(first_diffs_masked, sigma=5, axis=0)
     else:
         median_diffs = np.nanmedian(first_diffs_masked, axis=(0, 1))
         sigma = np.sqrt(np.abs(median_diffs) + read_noise_2 / nframes)
 
-    warnings.filterwarnings("ignore")
     for intg in range(nints):
         # calculate sigma for each pixel
         if nints < minimum_sigclip_groups:
diff --git a/src/stcal/jump/twopoint_difference.py b/src/stcal/jump/twopoint_difference.py
index 5d3e4705..5be6b595 100644
--- a/src/stcal/jump/twopoint_difference.py
+++ b/src/stcal/jump/twopoint_difference.py
@@ -240,7 +240,9 @@ def find_crs(
                 first_diffs = np.diff(dat, axis=1)
 
                 if total_usable_diffs >= min_diffs_single_pass:
+                    warnings.filterwarnings("ignore", ".*All-NaN slice encountered.*", RuntimeWarning)
                     median_diffs = np.nanmedian(first_diffs, axis=(0, 1))
+                    warnings.resetwarnings()
                     # calculate sigma for each pixel
                     sigma = np.sqrt(np.abs(median_diffs) + read_noise_2 / nframes)
                     # reset sigma so pixels with 0 read noise are not flagged as jumps