diff --git a/core/conversion/converters/impl/shuffle.cpp b/core/conversion/converters/impl/shuffle.cpp
index 5a8e992d90..8d25525a6e 100644
--- a/core/conversion/converters/impl/shuffle.cpp
+++ b/core/conversion/converters/impl/shuffle.cpp
@@ -20,7 +20,12 @@ static auto shuffle_registrations TORCHTRT_UNUSED =
                auto in_shape = util::toVec(in->getDimensions());
                std::vector<int64_t> out_shape;
                if (ctx->input_is_dynamic) {
-                 end_dim = (end_dim == -1) ? in_shape.size() - 1 : end_dim;
+                 if (start_dim < 0) {
+                   start_dim = start_dim + in_shape.size();
+                 }
+                 if (end_dim < 0) {
+                   end_dim = end_dim + in_shape.size();
+                 }
                  int nbDynamicFlattenedDims = 0;
                  int nbDynamicUnflattenedDims = 0;
                  for (int i = 0; i < (int)in_shape.size(); i++) {
diff --git a/tests/core/conversion/converters/test_shuffle.cpp b/tests/core/conversion/converters/test_shuffle.cpp
index 9c972ba988..a4d22e10c6 100644
--- a/tests/core/conversion/converters/test_shuffle.cpp
+++ b/tests/core/conversion/converters/test_shuffle.cpp
@@ -4,7 +4,6 @@
 #include "tests/util/util.h"
 #include "torch/csrc/jit/ir/irparser.h"
 
-// TODO: IR Parser doesnt work well with neg numbers
 TEST(Converters, ATenFlattenConvertsCorrectly) {
   const auto graph = R"IR(
     graph(%0 : Tensor):
@@ -23,12 +22,32 @@ TEST(Converters, ATenFlattenConvertsCorrectly) {
   in = at::clone(in);
   params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
   auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
-  auto trt = trt_results[0].reshape_as(jit_results[0]);
 
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
+}
+
+TEST(Converters, ATenFlattenNegDimsConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%0 : Tensor):
+      %1 : int = prim::Constant[value=-3]()
+      %2 : int = prim::Constant[value=-2]()
+      %3 : Tensor = aten::flatten(%0, %1, %2)
+      return (%3))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(0, 5, {2, 3, 3}, {at::kCUDA});
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
 }
 
-// TODO: IR Parser doesnt work well with neg numbers
 TEST(Converters, ATenFlattenOtherDimsConvertsCorrectly) {
   const auto graph = R"IR(
     graph(%0 : Tensor):
@@ -47,9 +66,8 @@ TEST(Converters, ATenFlattenOtherDimsConvertsCorrectly) {
   in = at::clone(in);
   params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
   auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
-  auto trt = trt_results[0].reshape_as(jit_results[0]);
 
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
 }
 
 TEST(Converters, ATenReshapeConvertsCorrectly) {
@@ -215,6 +233,29 @@ TEST(Converters, ATenFlattenConvertsCorrectlyWithDynamicBatch) {
   ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
 }
 
+TEST(Converters, ATenFlattenNegDimsConvertsCorrectlyWithDynamicBatch) {
+  const auto graph = R"IR(
+    graph(%0 : Tensor):
+      %1 : int = prim::Constant[value=-3]()
+      %2 : int = prim::Constant[value=-2]()
+      %3 : Tensor = aten::flatten(%0, %1, %2)
+      return (%3))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::randint(0, 5, {2, 3, 4}, {at::kCUDA});
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngineDynamic(g, params, {in}, true);
+  auto trt = trt_results[0].reshape_as(jit_results[0]);
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt, 2e-6));
+}
+
 TEST(Converters, ATenTransposeConvertsCorrectly) {
   const auto graph = R"IR(
     graph(%x.1 : Tensor):