diff --git a/core/conversion/converters/BUILD b/core/conversion/converters/BUILD
index 8b5338c08b..f9948eea7d 100755
--- a/core/conversion/converters/BUILD
+++ b/core/conversion/converters/BUILD
@@ -79,6 +79,7 @@ cc_library(
         "impl/squeeze.cpp",
         "impl/stack.cpp",
         "impl/topk.cpp",
+        "impl/max.cpp",
         "impl/unary.cpp",
         "impl/unsqueeze.cpp",
     ],
diff --git a/core/conversion/converters/impl/max.cpp b/core/conversion/converters/impl/max.cpp
new file mode 100644
index 0000000000..adc8d06ed0
--- /dev/null
+++ b/core/conversion/converters/impl/max.cpp
@@ -0,0 +1,43 @@
+#include "NvInfer.h"
+#include "core/conversion/converters/converters.h"
+#include "core/conversion/tensorcontainer/TensorContainer.h"
+#include "core/util/prelude.h"
+#include "torch/torch.h"
+
+#include <ATen/ATen.h>
+#include <vector>
+
+namespace torch_tensorrt {
+namespace core {
+namespace conversion {
+namespace converters {
+namespace impl {
+namespace {
+auto max_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
+    {"aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)",
+     [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+       auto self = args[0].ITensorOrFreeze(ctx);
+       auto dim = args[1].unwrapToInt();
+       auto selfDim = util::toVec(self->getDimensions());
+       if (dim < 0) {
+         dim = selfDim.size() + dim;
+       }
+       uint32_t shiftDim = 1 << dim;
+       auto TopKOperation = nvinfer1::TopKOperation::kMAX;
+       auto new_layer = ctx->net->addTopK(*self, TopKOperation, 1, shiftDim);
+       TORCHTRT_CHECK(new_layer, "Unable to create max layer from node: " << *n);
+
+       auto out0 = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
+       auto out1 = ctx->AssociateValueAndTensor(n->outputs()[1], new_layer->getOutput(1));
+
+       LOG_DEBUG("Output tensor(0) shape: " << out0->getDimensions());
+       LOG_DEBUG("Output tensor(1) shape: " << out1->getDimensions());
+
+       return true;
+     }});
+} // namespace
+} // namespace impl
+} // namespace converters
+} // namespace conversion
+} // namespace core
+} // namespace torch_tensorrt
diff --git a/tests/core/conversion/converters/test_topk.cpp b/tests/core/conversion/converters/test_topk.cpp
index dddddc9d2c..608e397a1f 100644
--- a/tests/core/conversion/converters/test_topk.cpp
+++ b/tests/core/conversion/converters/test_topk.cpp
@@ -30,3 +30,28 @@ TEST(Converters, ATenTopKConvertsCorrectly) {
   ASSERT_TRUE(
       torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6));
 }
+
+TEST(Converters, ATenMaxDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=0]()
+      %3 : bool = prim::Constant[value=0]()
+      %4 : Tensor, %5 : Tensor = aten::max(%x.1, %2, %3)
+      return (%4, %5))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {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].reshape_as(jit_results[0]), 2e-6));
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6));
+}