diff --git a/pytensor/link/jax/dispatch/nlinalg.py b/pytensor/link/jax/dispatch/nlinalg.py
index 68efb3e10e..26af73b137 100644
--- a/pytensor/link/jax/dispatch/nlinalg.py
+++ b/pytensor/link/jax/dispatch/nlinalg.py
@@ -3,7 +3,7 @@
 from pytensor.link.jax.dispatch import jax_funcify
 from pytensor.tensor.blas import BatchedDot
 from pytensor.tensor.math import Dot, MaxAndArgmax
-from pytensor.tensor.nlinalg import SVD, Det, Eig, Eigh, MatrixInverse, QRFull
+from pytensor.tensor.nlinalg import SVD, Det, Eig, Eigh, MatrixInverse, QRFull, SLogDet
 
 
 @jax_funcify.register(SVD)
@@ -25,6 +25,14 @@ def det(x):
     return det
 
 
+@jax_funcify.register(SLogDet)
+def jax_funcify_SLogDet(op, **kwargs):
+    def slogdet(x):
+        return jnp.linalg.slogdet(x)
+
+    return slogdet
+
+
 @jax_funcify.register(Eig)
 def jax_funcify_Eig(op, **kwargs):
     def eig(x):
diff --git a/pytensor/link/numba/dispatch/nlinalg.py b/pytensor/link/numba/dispatch/nlinalg.py
index 21fa34e1bb..174c684d06 100644
--- a/pytensor/link/numba/dispatch/nlinalg.py
+++ b/pytensor/link/numba/dispatch/nlinalg.py
@@ -18,6 +18,7 @@
     MatrixInverse,
     MatrixPinv,
     QRFull,
+    SLogDet,
 )
 
 
@@ -58,6 +59,25 @@ def det(x):
     return det
 
 
+@numba_funcify.register(SLogDet)
+def numba_funcify_SLogDet(op, node, **kwargs):
+
+    out_dtype_1 = node.outputs[0].type.numpy_dtype
+    out_dtype_2 = node.outputs[1].type.numpy_dtype
+
+    inputs_cast = int_to_float_fn(node.inputs, out_dtype_1)
+
+    @numba_basic.numba_njit
+    def slogdet(x):
+        sign, det = np.linalg.slogdet(inputs_cast(x))
+        return (
+            numba_basic.direct_cast(sign, out_dtype_1),
+            numba_basic.direct_cast(det, out_dtype_2),
+        )
+
+    return slogdet
+
+
 @numba_funcify.register(Eig)
 def numba_funcify_Eig(op, node, **kwargs):
 
diff --git a/pytensor/tensor/nlinalg.py b/pytensor/tensor/nlinalg.py
index 799f0fcf54..7da8bb044d 100644
--- a/pytensor/tensor/nlinalg.py
+++ b/pytensor/tensor/nlinalg.py
@@ -231,6 +231,39 @@ def __str__(self):
 det = Det()
 
 
+class SLogDet(Op):
+    """
+    Compute the log determinant and its sign of the matrix. Input should be a square matrix.
+    """
+
+    __props__ = ()
+
+    def make_node(self, x):
+        x = as_tensor_variable(x)
+        assert x.ndim == 2
+        sign = scalar(dtype=x.dtype)
+        det = scalar(dtype=x.dtype)
+        return Apply(self, [x], [sign, det])
+
+    def perform(self, node, inputs, outputs):
+        (x,) = inputs
+        (sign, det) = outputs
+        try:
+            sign[0], det[0] = (z.astype(x.dtype) for z in np.linalg.slogdet(x))
+        except Exception:
+            print("Failed to compute determinant", x)
+            raise
+
+    def infer_shape(self, fgraph, node, shapes):
+        return [(), ()]
+
+    def __str__(self):
+        return "SLogDet"
+
+
+slogdet = SLogDet()
+
+
 class Eig(Op):
     """
     Compute the eigenvalues and right eigenvectors of a square array.
diff --git a/tests/link/jax/test_nlinalg.py b/tests/link/jax/test_nlinalg.py
index 50d1cf378e..146766f400 100644
--- a/tests/link/jax/test_nlinalg.py
+++ b/tests/link/jax/test_nlinalg.py
@@ -85,6 +85,10 @@ def assert_fn(x, y):
     out_fg = FunctionGraph([x], outs)
     compare_jax_and_py(out_fg, [X.astype(config.floatX)], assert_fn=assert_fn)
 
+    outs = at_nlinalg.slogdet(x)
+    out_fg = FunctionGraph([x], outs)
+    compare_jax_and_py(out_fg, [X.astype(config.floatX)], assert_fn=assert_fn)
+
 
 @pytest.mark.xfail(
     version_parse(jax.__version__) >= version_parse("0.2.12"),
diff --git a/tests/link/numba/test_nlinalg.py b/tests/link/numba/test_nlinalg.py
index 3018b9a97a..7bc60d1313 100644
--- a/tests/link/numba/test_nlinalg.py
+++ b/tests/link/numba/test_nlinalg.py
@@ -179,6 +179,41 @@ def test_Det(x, exc):
         )
 
 
+@pytest.mark.parametrize(
+    "x, exc",
+    [
+        (
+            set_test_value(
+                at.dmatrix(),
+                (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
+            ),
+            None,
+        ),
+        (
+            set_test_value(
+                at.lmatrix(),
+                (lambda x: x.T.dot(x))(rng.poisson(size=(3, 3)).astype("int64")),
+            ),
+            None,
+        ),
+    ],
+)
+def test_SLogDet(x, exc):
+    g = nlinalg.SLogDet()(x)
+    g_fg = FunctionGraph(outputs=g)
+
+    cm = contextlib.suppress() if exc is None else pytest.warns(exc)
+    with cm:
+        compare_numba_and_py(
+            g_fg,
+            [
+                i.tag.test_value
+                for i in g_fg.inputs
+                if not isinstance(i, (SharedVariable, Constant))
+            ],
+        )
+
+
 # We were seeing some weird results in CI where the following two almost
 # sign-swapped results were being return from Numba and Python, respectively.
 # The issue might be related to https://github.com/numba/numba/issues/4519.
diff --git a/tests/tensor/test_nlinalg.py b/tests/tensor/test_nlinalg.py
index b39ac0ba3c..15dbbff083 100644
--- a/tests/tensor/test_nlinalg.py
+++ b/tests/tensor/test_nlinalg.py
@@ -24,6 +24,7 @@
     norm,
     pinv,
     qr,
+    slogdet,
     svd,
     tensorinv,
     tensorsolve,
@@ -280,6 +281,18 @@ def test_det_shape():
     assert tuple(det_shape.data) == ()
 
 
+def test_slogdet():
+    rng = np.random.default_rng(utt.fetch_seed())
+
+    r = rng.standard_normal((5, 5)).astype(config.floatX)
+    x = matrix()
+    f = pytensor.function([x], slogdet(x))
+    f_sign, f_det = f(r)
+    sign, det = np.linalg.slogdet(r)
+    assert np.equal(sign, f_sign)
+    assert np.allclose(det, f_det)
+
+
 def test_trace():
     rng = np.random.default_rng(utt.fetch_seed())
     x = matrix()