diff --git a/pytensor/link/mlx/dispatch/signal/conv.py b/pytensor/link/mlx/dispatch/signal/conv.py
index 481457d4da..eafc0bb87b 100644
--- a/pytensor/link/mlx/dispatch/signal/conv.py
+++ b/pytensor/link/mlx/dispatch/signal/conv.py
@@ -21,6 +21,13 @@ def conv1d(raw_data, raw_kernel, runtime_full_mode):
         data = mlx_typify(raw_data, dtype=None)
         kernel = mlx_typify(raw_kernel, dtype=None)
 
+        # Inside a Blockwise, a kernel that is broadcast across the batch is
+        # promoted to a leading-1 dim (e.g. ``(1, K)``) instead of being
+        # vmapped away. ``mx.convolve`` needs 1-D inputs, so flatten the kernel
+        # back to its core ``(K,)`` shape.
+        if kernel.ndim > 1:
+            kernel = kernel.reshape(-1)
+
         if runtime_mode_static:
             runtime_mode = full_mode
         else:
diff --git a/tests/link/mlx/test_signal_conv.py b/tests/link/mlx/test_signal_conv.py
new file mode 100644
index 0000000000..844be2711a
--- /dev/null
+++ b/tests/link/mlx/test_signal_conv.py
@@ -0,0 +1,41 @@
+import numpy as np
+import pytest
+
+import pytensor.tensor as pt
+from pytensor.tensor.type import matrix, vector
+from tests.link.mlx.test_basic import compare_mlx_and_py
+
+
+pytest.importorskip("mlx.core")
+
+
+@pytest.mark.parametrize("mode", ["full", "valid"])
+def test_convolve1d_vector(mode):
+    x = vector("x", dtype="float32")
+    k = vector("k", dtype="float32")
+    out = pt.signal.conv.convolve1d(x, k, mode=mode)
+
+    rng = np.random.default_rng(0)
+    x_np = rng.standard_normal(32).astype("float32")
+    k_np = rng.standard_normal(5).astype("float32")
+
+    compare_mlx_and_py([x, k], [out], [x_np, k_np])
+
+
+@pytest.mark.parametrize("mode", ["full", "valid"])
+def test_convolve1d_batched_kernel_broadcast(mode):
+    """A vector kernel shared across a batch of signals is wrapped in a
+    Blockwise that broadcasts it to a leading-1 dim, so the MLX core thunk
+    must flatten it back to 1-D before calling ``mx.convolve``.
+
+    Regression test for #2092.
+    """
+    x = matrix("x", dtype="float32")
+    k = vector("k", dtype="float32")
+    out = pt.signal.conv.convolve1d(x, k, mode=mode)
+
+    rng = np.random.default_rng(0)
+    x_np = rng.standard_normal((4, 32)).astype("float32")
+    k_np = rng.standard_normal(5).astype("float32")
+
+    compare_mlx_and_py([x, k], [out], [x_np, k_np])