diff --git a/tfjs-node/src/index.ts b/tfjs-node/src/index.ts
index 2ca6bbf87fd..6533cde569f 100644
--- a/tfjs-node/src/index.ts
+++ b/tfjs-node/src/index.ts
@@ -24,6 +24,9 @@ import {NodeJSKernelBackend} from './nodejs_kernel_backend';
 import {TFJSBinding} from './tfjs_binding';
 import * as nodeVersion from './version';
 
+// Import all kernels.
+import './kernels/all_kernels';
+
 // tslint:disable-next-line:no-require-imports
 const binary = require('node-pre-gyp');
 const bindingPath =
diff --git a/tfjs-node/src/kernels/all_kernels.ts b/tfjs-node/src/kernels/all_kernels.ts
new file mode 100644
index 00000000000..c6eea842964
--- /dev/null
+++ b/tfjs-node/src/kernels/all_kernels.ts
@@ -0,0 +1,21 @@
+/**
+ * @license
+ * Copyright 2019 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+// We explicitly import the modular kernels so they get registered in the
+// global registry when we compile the library. A modular build would replace
+// the contents of this file and import only the kernels that are needed.
+import './non_max_suppression_v5';
diff --git a/tfjs-node/src/kernels/non_max_suppression_v5.ts b/tfjs-node/src/kernels/non_max_suppression_v5.ts
new file mode 100644
index 00000000000..6f6dbb46b89
--- /dev/null
+++ b/tfjs-node/src/kernels/non_max_suppression_v5.ts
@@ -0,0 +1,67 @@
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import {NamedAttrMap, NamedTensorInfoMap, registerKernel, scalar, Tensor1D, Tensor2D, TensorInfo} from '@tensorflow/tfjs-core';
+
+import {createTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
+
+interface NonMaxSuppressionWithScoreInputs extends NamedTensorInfoMap {
+  boxes: TensorInfo;
+  scores: TensorInfo;
+}
+
+interface NonMaxSuppressionWithScoreAttrs extends NamedAttrMap {
+  maxOutputSize: number;
+  iouThreshold: number;
+  scoreThreshold: number;
+  softNmsSigma: number;
+}
+
+// TODO(nsthorat, dsmilkov): Remove dependency on tensors, use dataId.
+registerKernel({
+  kernelName: 'NonMaxSuppressionV5',
+  backendName: 'tensorflow',
+  kernelFunc: ({inputs, backend, attrs}) => {
+    const {boxes, scores} = inputs as NonMaxSuppressionWithScoreInputs;
+    const {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma} =
+        attrs as NonMaxSuppressionWithScoreAttrs;
+    const maxOutputSizeTensor = scalar(maxOutputSize, 'int32');
+    const iouThresholdTensor = scalar(iouThreshold);
+    const scoreThresholdTensor = scalar(scoreThreshold);
+    const softNmsSigmaTensor = scalar(softNmsSigma);
+    const opAttrs = [createTypeOpAttr('T', boxes.dtype)];
+
+    const nodeBackend = backend as NodeJSKernelBackend;
+
+    const [selectedIndices, selectedScores, validOutputs] =
+        nodeBackend.executeMultipleOutputs(
+            'NonMaxSuppressionV5', opAttrs,
+            [
+              boxes as Tensor2D, scores as Tensor1D, maxOutputSizeTensor,
+              iouThresholdTensor, scoreThresholdTensor, softNmsSigmaTensor
+            ],
+            3);
+
+    maxOutputSizeTensor.dispose();
+    iouThresholdTensor.dispose();
+    scoreThresholdTensor.dispose();
+    softNmsSigmaTensor.dispose();
+    validOutputs.dispose();
+
+    return [selectedIndices, selectedScores];
+  }
+});