diff --git a/components/google-cloud/RELEASE.md b/components/google-cloud/RELEASE.md
index cb51fb6e857b..56ff2c9b55dc 100644
--- a/components/google-cloud/RELEASE.md
+++ b/components/google-cloud/RELEASE.md
@@ -6,6 +6,7 @@
 * Add support for running tasks on a `PersistentResource` (see [CustomJobSpec](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/CustomJobSpec)) via `persistent_resource_id` parameter on `v1.custom_job.CustomTrainingJobOp` and `v1.custom_job.create_custom_training_job_from_component`
 * Bump image for Structured Data pipelines.
 * Add check that component in preview.custom_job.utils.create_custom_training_job_from_component doesn't have any parameters that share names with any custom job fields
+* Add dynamic machine spec support for `preview.custom_job.utils.create_custom_training_job_from_component`.
 
 ## Release 2.15.0
 * Add Gemini batch prediction support to `v1.model_evaluation.autosxs_pipeline`.
diff --git a/components/google-cloud/google_cloud_pipeline_components/container/preview/custom_job/remote_runner.py b/components/google-cloud/google_cloud_pipeline_components/container/preview/custom_job/remote_runner.py
index d72f35833a1b..e56548c002fe 100644
--- a/components/google-cloud/google_cloud_pipeline_components/container/preview/custom_job/remote_runner.py
+++ b/components/google-cloud/google_cloud_pipeline_components/container/preview/custom_job/remote_runner.py
@@ -32,6 +32,27 @@ def insert_system_labels_into_payload(payload):
   return json.dumps(job_spec)
 
 
+def cast_accelerator_count_to_int(payload):
+  """Casts accelerator_count from string to an int."""
+
+  job_spec = json.loads(payload)
+  # TODO(b/353577594): accelerator_count placeholder is not resolved to int.
+  # Need to typecast to int to avoid type mismatch error. Can remove when fix
+  # placeholder resolution.
+  if (
+      'accelerator_count'
+      in job_spec['job_spec']['worker_pool_specs'][0]['machine_spec']
+  ):
+    job_spec['job_spec']['worker_pool_specs'][0]['machine_spec'][
+        'accelerator_count'
+    ] = int(
+        job_spec['job_spec']['worker_pool_specs'][0]['machine_spec'][
+            'accelerator_count'
+        ]
+    )
+  return json.dumps(job_spec)
+
+
 def create_custom_job_with_client(job_client, parent, job_spec):
   create_custom_job_fn = None
   try:
@@ -86,6 +107,7 @@ def create_custom_job(
     # Create custom job if it does not exist
     job_name = remote_runner.check_if_job_exists()
     if job_name is None:
+      payload = cast_accelerator_count_to_int(payload)
       job_name = remote_runner.create_job(
           create_custom_job_with_client,
           insert_system_labels_into_payload(payload),
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/custom_job/utils.py b/components/google-cloud/google_cloud_pipeline_components/preview/custom_job/utils.py
index dc78cf8dee79..ed35a559f557 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/custom_job/utils.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/custom_job/utils.py
@@ -54,7 +54,7 @@ def create_custom_training_job_from_component(
     display_name: str = '',
     replica_count: int = 1,
     machine_type: str = 'n1-standard-4',
-    accelerator_type: str = '',
+    accelerator_type: str = 'ACCELERATOR_TYPE_UNSPECIFIED',
     accelerator_count: int = 1,
     boot_disk_type: str = 'pd-ssd',
     boot_disk_size_gb: int = 100,
@@ -83,7 +83,7 @@ def create_custom_training_job_from_component(
       replica_count: The count of instances in the cluster. One replica always counts towards the master in worker_pool_spec[0] and the remaining replicas will be allocated in worker_pool_spec[1]. See [more information.](https://cloud.google.com/vertex-ai/docs/training/distributed-training#configure_a_distributed_training_job)
       machine_type: The type of the machine to run the CustomJob. The default value is "n1-standard-4". See [more information](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types).
       accelerator_type: The type of accelerator(s) that may be attached to the machine per `accelerator_count`. See [more information](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#acceleratortype).
-      accelerator_count: The number of accelerators to attach to the machine. Defaults to 1 if `accelerator_type` is set.
+      accelerator_count: The number of accelerators to attach to the machine. Defaults to 1 if `accelerator_type` is set statically.
       boot_disk_type: Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive). boot_disk_type is set as a static value and cannot be changed as a pipeline parameter.
       boot_disk_size_gb: Size in GB of the boot disk (default is 100GB). `boot_disk_size_gb` is set as a static value and cannot be changed as a pipeline parameter.
       timeout: The maximum job running time. The default is 7 days. A duration in seconds with up to nine fractional digits, terminated by 's', for example: "3.5s".
@@ -148,7 +148,11 @@ def create_custom_training_job_from_component(
   )[0]['container']
 
   worker_pool_spec = {
-      'machine_spec': {'machine_type': machine_type},
+      'machine_spec': {
+          'machine_type': "{{$.inputs.parameters['machine_type']}}",
+          'accelerator_type': "{{$.inputs.parameters['accelerator_type']}}",
+          'accelerator_count': "{{$.inputs.parameters['accelerator_count']}}",
+      },
       'replica_count': 1,
       'container_spec': {
           'image_uri': user_component_container['image'],
@@ -161,9 +165,6 @@ def create_custom_training_job_from_component(
           'env': env or [],
       },
   }
-  if accelerator_type:
-    worker_pool_spec['machine_spec']['accelerator_type'] = accelerator_type
-    worker_pool_spec['machine_spec']['accelerator_count'] = accelerator_count
   if boot_disk_type:
     worker_pool_spec['disk_spec'] = {
         'boot_disk_type': boot_disk_type,
@@ -210,6 +211,26 @@ def create_custom_training_job_from_component(
         'defaultValue'
     ] = default_value
 
+  # add machine parameters into the customjob component
+  if accelerator_type == 'ACCELERATOR_TYPE_UNSPECIFIED':
+    accelerator_count = 0
+
+  cj_component_spec['inputDefinitions']['parameters']['machine_type'] = {
+      'parameterType': 'STRING',
+      'defaultValue': machine_type,
+      'isOptional': True,
+  }
+  cj_component_spec['inputDefinitions']['parameters']['accelerator_type'] = {
+      'parameterType': 'STRING',
+      'defaultValue': accelerator_type,
+      'isOptional': True,
+  }
+  cj_component_spec['inputDefinitions']['parameters']['accelerator_count'] = {
+      'parameterType': 'NUMBER_INTEGER',
+      'defaultValue': accelerator_count,
+      'isOptional': True,
+  }
+
   # check if user component has any input parameters that already exist in the
   # custom job component
   for param_name in user_component_spec.get('inputDefinitions', {}).get(
@@ -234,6 +255,7 @@ def create_custom_training_job_from_component(
   cj_component_spec['outputDefinitions']['parameters'].update(
       user_component_spec.get('outputDefinitions', {}).get('parameters', {})
   )
+
   # use artifacts from user component
   ## assign artifacts, not update, since customjob has no artifact outputs
   cj_component_spec['inputDefinitions']['artifacts'] = user_component_spec.get(