diff --git a/joblib/parallel.py b/joblib/parallel.py
index af842b42c..ef8d3ab19 100644
--- a/joblib/parallel.py
+++ b/joblib/parallel.py
@@ -100,6 +100,8 @@ def _register_dask():
     "mmap_mode": _Sentinel(default_value="r"),
     "prefer": _Sentinel(default_value=None),
     "require": _Sentinel(default_value=None),
+    "initializer": _Sentinel(default_value=None),
+    "initargs": _Sentinel(default_value=()),
 }
 
 
@@ -312,6 +314,16 @@ class parallel_config:
         usable in some third-party library threadpools like OpenBLAS,
         MKL or OpenMP. This is only used with the ``loky`` backend.
 
+    initializer : callable, default=None
+        If not None, this will be called at the start of each worker
+        process. This can be used with the ``loky`` and
+        ``multiprocessing`` backends. Note that when workers are
+        reused, as is done by the ``loky`` backend, initialization
+        happens only once per worker.
+
+    initargs : tuple, default=None
+        Arguments for initializer.
+
     backend_params : dict
         Additional parameters to pass to the backend constructor when
         backend is a string.
@@ -356,6 +368,8 @@ def __init__(
         prefer=default_parallel_config["prefer"],
         require=default_parallel_config["require"],
         inner_max_num_threads=None,
+        initializer=default_parallel_config["initializer"],
+        initargs=default_parallel_config["initargs"],
         **backend_params
     ):
         # Save the parallel info and set the active parallel config
@@ -375,7 +389,9 @@ def __init__(
             "mmap_mode": mmap_mode,
             "prefer": prefer,
             "require": require,
-            "backend": backend
+            "backend": backend,
+            "initializer": initializer,
+            "initargs": initargs,
         }
         self.parallel_config = self.old_parallel_config.copy()
         self.parallel_config.update({
@@ -1057,6 +1073,14 @@ class Parallel(Logger):
             disable memmapping, other modes defined in the numpy.memmap doc:
             https://numpy.org/doc/stable/reference/generated/numpy.memmap.html
             Also, see 'max_nbytes' parameter documentation for more details.
+        initializer : callable, default=None
+            If not None, this will be called at the start of each
+            worker process. This can be used with the ``loky`` and
+            ``multiprocessing`` backends. Note that when workers are
+            reused, as is done by the ``loky`` backend, initialization
+            happens only once per worker.
+        initargs : tuple, default=None
+            Arguments for initializer.
 
         Notes
         -----
@@ -1194,6 +1218,8 @@ def __init__(
         mmap_mode=default_parallel_config["mmap_mode"],
         prefer=default_parallel_config["prefer"],
         require=default_parallel_config["require"],
+        initializer=default_parallel_config["initializer"],
+        initargs=default_parallel_config["initargs"],
     ):
         # Initiate parent Logger class state
         super().__init__()
@@ -1233,6 +1259,8 @@ def __init__(
                 (prefer, "prefer"),
                 (require, "require"),
                 (verbose, "verbose"),
+                (initializer, "initializer"),
+                (initargs, "initargs"),
             ]
         }
 
diff --git a/joblib/test/test_parallel.py b/joblib/test/test_parallel.py
index e2d6e6f6a..b8cdae2e3 100644
--- a/joblib/test/test_parallel.py
+++ b/joblib/test/test_parallel.py
@@ -6,6 +6,7 @@
 # Copyright (c) 2010-2011 Gael Varoquaux
 # License: BSD Style, 3 clauses.
 
+import multiprocessing
 import os
 import sys
 import time
@@ -520,7 +521,7 @@ def producer():
             queue.append('Produced %i' % i)
             yield i
 
-    Parallel(n_jobs=2, batch_size=1, pre_dispatch=3, backend=backend)(
+    Parallel(n_jobs=12, batch_size=1, pre_dispatch=3, backend=backend)(
         delayed(consumer)(queue, 'any') for _ in producer())
 
     queue_contents = list(queue)
@@ -2008,3 +2009,56 @@ def parallel_call(n_jobs):
         parallel_call(n_jobs)
         executor = get_reusable_executor(reuse=True)
         assert executor == first_executor
+
+
+@with_multiprocessing
+@parametrize('n_jobs', [2, 4, -1])
+@parametrize('backend', PROCESS_BACKENDS)
+@parametrize("context", [parallel_config, parallel_backend])
+def test_initializer(n_jobs, backend, context):
+    n_jobs = effective_n_jobs(n_jobs)
+    manager = mp.Manager()
+    queue = manager.list()
+
+    def initializer(queue):
+        queue.append("spam")
+
+    with context(
+        backend=backend,
+        n_jobs=n_jobs,
+        initializer=initializer,
+        initargs=(queue,)
+    ):
+        with Parallel() as parallel:
+            values = parallel(delayed(square)(i) for i in range(n_jobs))
+
+    assert len(queue) == n_jobs
+    assert all(q == "spam" for q in queue)
+
+
+@with_multiprocessing
+@pytest.mark.parametrize("n_jobs", [2, 4, -1])
+def test_initializer_reuse(n_jobs):
+    # test that the initializer is called only once
+    # when the executor is reused
+    n_jobs = effective_n_jobs(n_jobs)
+    manager = mp.Manager()
+    queue = manager.list()
+
+    def initializer(queue):
+        queue.append("spam")
+
+    def parallel_call(n_jobs, initializer, initargs):
+        Parallel(
+            backend="loky",
+            n_jobs=n_jobs,
+            initializer=initializer,
+            initargs=(queue,),
+        )(delayed(square)(i) for i in range(n_jobs))
+
+    parallel_call(n_jobs, initializer, (queue,))
+    assert len(queue) == n_jobs
+
+    for i in range(10):
+        parallel_call(n_jobs, initializer, (queue,))
+        assert len(queue) == n_jobs