diff --git a/mlx/primitives.cpp b/mlx/primitives.cpp
index eb5d9d6b3..72affbd34 100644
--- a/mlx/primitives.cpp
+++ b/mlx/primitives.cpp
@@ -2459,7 +2459,7 @@ std::vector<array> Imag::vjp(
   assert(primals.size() == 1);
   assert(argnums.size() == 1);
   return {multiply(
-      array(complex64_t{0.0f, -1.0f}, primals[0].dtype()),
+      array(complex64_t{0.0f, 1.0f}, primals[0].dtype()),
       cotangents[0],
       stream())};
 }
@@ -2788,15 +2788,19 @@ std::vector<array> Matmul::vjp(
   std::vector<int> reorder(cotan.ndim());
   std::iota(reorder.begin(), reorder.end(), 0);
   std::iter_swap(reorder.end() - 1, reorder.end() - 2);
+  auto& s = stream();
+
+  auto complex_transpose = [&](const array& x) {
+    return transpose(conjugate(x, s), reorder, s);
+  };
+
   for (auto arg : argnums) {
     if (arg == 0) {
       // M X N * (K X N).T -> M X K
-      vjps.push_back(
-          matmul(cotan, transpose(primals[1], reorder, stream()), stream()));
+      vjps.push_back(matmul(cotan, complex_transpose(primals[1]), s));
     } else {
       // (M X K).T * M X N -> K X N
-      vjps.push_back(
-          matmul(transpose(primals[0], reorder, stream()), cotan, stream()));
+      vjps.push_back(matmul(complex_transpose(primals[0]), cotan, s));
     }
   }
   return vjps;
diff --git a/python/tests/test_autograd.py b/python/tests/test_autograd.py
index 7973d79be..5722071f6 100644
--- a/python/tests/test_autograd.py
+++ b/python/tests/test_autograd.py
@@ -606,7 +606,7 @@ class TestAutograd(mlx_tests.MLXTestCase):
 
         x = mx.array([0.0 + 1j, 1.0 + 0.0j, 0.5 + 0.5j])
         dfdx = mx.grad(fun)(x)
-        self.assertTrue(mx.allclose(dfdx, -2j * mx.ones_like(x)))
+        self.assertTrue(mx.allclose(dfdx, 2j * mx.ones_like(x)))
 
     def test_flatten_unflatten_vjps(self):
         def fun(x):