diff --git a/src/crested/tl/losses/__init__.py b/src/crested/tl/losses/__init__.py
index 3324497..c1d24ad 100644
--- a/src/crested/tl/losses/__init__.py
+++ b/src/crested/tl/losses/__init__.py
@@ -2,3 +2,4 @@
 
 from ._cosinemse import CosineMSELoss
 from ._cosinemse_log import CosineMSELogLoss
+from ._poisson import PoissonLoss
diff --git a/src/crested/tl/losses/_poisson.py b/src/crested/tl/losses/_poisson.py
new file mode 100644
index 0000000..aeef9af
--- /dev/null
+++ b/src/crested/tl/losses/_poisson.py
@@ -0,0 +1,88 @@
+import keras
+import keras.ops as ops
+
+
+@keras.saving.register_keras_serializable(package="Losses")
+class PoissonLoss(keras.losses.Loss):
+    """
+    Custom Poisson loss for count data with optional log(x + 1) transformation.
+
+    This loss function computes the Poisson loss, optionally applying
+    log(x + 1) transformations to predictions and/or targets to ensure
+    non-negativity.
+
+    Parameters
+    ----------
+    log_transform : bool
+        If True, applies log(x + 1) transformation to both predictions and targets.
+    eps : float
+        Small value to avoid log(0).
+    reduction : str
+        Type of reduction to apply to the loss. Default: "sum_over_batch_size".
+    """
+
+    def __init__(
+        self,
+        log_transform: bool = True,
+        eps: float = 1e-7,
+        reduction: str = "sum_over_batch_size",
+        name: str = "PoissonLoss"
+    ):
+        """
+        Initialize the PoissonLoss class.
+
+        Parameters
+        ----------
+        log_transform : bool, optional
+            Whether to apply a log(x + 1) transformation to the inputs. Default is True.
+        eps : float, optional
+            A small epsilon value to avoid log(0). Default is 1e-7.
+        reduction : str, optional
+            The type of reduction to apply to the loss, e.g., "sum_over_batch_size".
+            Default is "sum_over_batch_size".
+        name : str, optional
+            Name of the loss function. Default is "PoissonLoss".
+        """
+        super().__init__(name=name, reduction=reduction)
+        self.log_transform = log_transform
+        self.eps = eps
+
+    def call(self, y_true, y_pred):
+        """
+        Compute the Poisson loss.
+
+        Parameters
+        ----------
+        y_true : Tensor
+            True target values (counts or log(x + 1)-transformed counts).
+        y_pred : Tensor
+            Predicted values (counts or log(x + 1)-transformed counts).
+
+        Returns
+        -------
+        Tensor
+            The Poisson loss value for each sample.
+        """
+        # Ensure predictions and targets are float32
+        y_true = ops.cast(y_true, dtype="float32")
+        y_pred = ops.cast(y_pred, dtype="float32")
+
+        # Apply log(x + 1) transformation if needed
+        if self.log_transform:
+            y_true = ops.log(y_true + 1.0)
+            y_pred = ops.log(y_pred + 1.0)
+
+        # Compute Poisson loss for each class
+        loss = y_pred - y_true * ops.log(y_pred + self.eps)
+
+        # Sum the loss across classes
+        return ops.sum(loss, axis=-1)
+
+    def get_config(self):
+        """Return the configuration of the loss function."""
+        config = super().get_config()
+        config.update({
+            "log_transform": self.log_transform,
+            "eps": self.eps
+        })
+        return config