diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6fa302b..cfa8a91 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,10 @@ after its first published distribution.
 - Public API stability policy for tier-1 modules.
 - README Python API example coverage through a contract test.
 - Changelog discipline before first publish.
+- Internal masked uncertainty-weighted multitask loss for auxiliary training
+  heads, combining only the targets each sample actually carries.
+- Internal hierarchical utterance sampling primitives: square-root corpus mass,
+  inverse-square-root class mass, and bounded seeded window selection.
 
 ### Changed
 
diff --git a/ser/_internal/heads/multitask_loss.py b/ser/_internal/heads/multitask_loss.py
new file mode 100644
index 0000000..babb202
--- /dev/null
+++ b/ser/_internal/heads/multitask_loss.py
@@ -0,0 +1,65 @@
+"""Masked uncertainty-weighted objectives for auxiliary training heads."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+
+import torch
+from torch import Tensor, nn
+
+
+class MaskedUncertaintyWeightedLoss(nn.Module):
+    """Combines available per-sample task losses with learned uncertainty weights."""
+
+    def __init__(
+        self,
+        tasks: Sequence[str],
+        *,
+        primary_task: str = "primary_emotion",
+        minimum_primary_weight: float = 0.25,
+    ) -> None:
+        """Initializes trainable log variances for a fixed task set."""
+        super().__init__()
+        normalized_tasks = tuple(dict.fromkeys(task.strip() for task in tasks if task.strip()))
+        if not normalized_tasks:
+            raise ValueError("At least one multitask objective is required.")
+        if not 0.0 < minimum_primary_weight <= 1.0:
+            raise ValueError("minimum_primary_weight must be within (0, 1].")
+        if any("." in task for task in normalized_tasks):
+            raise ValueError("Task names cannot contain '.'.")
+        self.primary_task = primary_task
+        self.minimum_primary_weight = minimum_primary_weight
+        self.log_variances = nn.ParameterDict(
+            {task: nn.Parameter(torch.zeros((), dtype=torch.float32)) for task in normalized_tasks}
+        )
+
+    def forward(
+        self,
+        losses: Mapping[str, Tensor],
+        masks: Mapping[str, Tensor],
+    ) -> Tensor:
+        """Returns a scalar loss using only targets marked available by each mask."""
+        total: Tensor | None = None
+        active_tasks = 0
+        for task, log_variance in self.log_variances.items():
+            if task not in losses or task not in masks:
+                continue
+            task_losses = losses[task]
+            mask = masks[task]
+            if task_losses.ndim == 0:
+                task_losses = task_losses.unsqueeze(0)
+            if mask.shape != task_losses.shape:
+                raise ValueError(f"Loss and mask shapes differ for task {task!r}.")
+            active = mask.to(dtype=torch.bool)
+            if not bool(torch.any(active)):
+                continue
+            mean_loss = task_losses[active].mean()
+            weight = torch.exp(-log_variance)
+            if task == self.primary_task:
+                weight = torch.clamp_min(weight, self.minimum_primary_weight)
+            weighted = weight * mean_loss + log_variance
+            total = weighted if total is None else total + weighted
+            active_tasks += 1
+        if total is None or active_tasks == 0:
+            raise ValueError("No available targets were supplied to the multitask loss.")
+        return total
diff --git a/ser/_internal/models/utterance_sampling.py b/ser/_internal/models/utterance_sampling.py
new file mode 100644
index 0000000..612b48a
--- /dev/null
+++ b/ser/_internal/models/utterance_sampling.py
@@ -0,0 +1,120 @@
+"""Utterance-level corpus/class sampling and bounded window selection."""
+
+from __future__ import annotations
+
+import hashlib
+import math
+import random
+from collections import Counter, defaultdict
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class UtteranceSamplingItem:
+    """Minimal utterance metadata needed by the balanced sampler."""
+
+    sample_id: str
+    corpus: str
+    label: str
+    window_count: int
+    duration_seconds: float | None = None
+
+    def validate(self) -> None:
+        """Validates item identity and bounded integer window count."""
+        if not self.sample_id.strip() or not self.corpus.strip() or not self.label.strip():
+            raise ValueError("Sampling item identifiers and label must be non-empty.")
+        if self.window_count <= 0:
+            raise ValueError("Sampling item window_count must be positive.")
+        if self.duration_seconds is not None and self.duration_seconds <= 0.0:
+            raise ValueError("Sampling item duration_seconds must be positive when provided.")
+
+
+@dataclass(frozen=True)
+class SamplingProbability:
+    """Expected contribution of one utterance under hierarchical sampling."""
+
+    sample_id: str
+    corpus: str
+    label: str
+    probability: float
+
+
+def utterance_sampling_distribution(
+    items: list[UtteranceSamplingItem],
+) -> tuple[SamplingProbability, ...]:
+    """Computes ``sqrt(corpus)`` and inverse-``sqrt(class)`` sampling probabilities."""
+    if not items:
+        raise ValueError("Cannot build a sampling distribution for an empty dataset.")
+    sample_ids: set[str] = set()
+    corpus_counts: Counter[str] = Counter()
+    class_counts: dict[str, Counter[str]] = defaultdict(Counter)
+    for item in items:
+        item.validate()
+        if item.sample_id in sample_ids:
+            raise ValueError(f"Duplicate sampling sample_id {item.sample_id!r}.")
+        sample_ids.add(item.sample_id)
+        corpus_counts[item.corpus] += 1
+        class_counts[item.corpus][item.label] += 1
+
+    corpus_normalizer = sum(math.sqrt(count) for count in corpus_counts.values())
+    class_normalizers = {
+        corpus: sum(1.0 / math.sqrt(count) for count in counts.values())
+        for corpus, counts in class_counts.items()
+    }
+    probabilities = []
+    for item in items:
+        corpus_probability = math.sqrt(corpus_counts[item.corpus]) / corpus_normalizer
+        label_count = class_counts[item.corpus][item.label]
+        class_probability = (1.0 / math.sqrt(label_count)) / class_normalizers[item.corpus]
+        item_probability = corpus_probability * class_probability / label_count
+        probabilities.append(
+            SamplingProbability(item.sample_id, item.corpus, item.label, item_probability)
+        )
+    total = sum(row.probability for row in probabilities)
+    if not math.isclose(total, 1.0, rel_tol=1e-12, abs_tol=1e-12):
+        raise RuntimeError(f"Sampling probabilities do not sum to one: {total!r}.")
+    return tuple(sorted(probabilities, key=lambda row: row.sample_id))
+
+
+def select_training_windows(
+    *,
+    sample_id: str,
+    window_count: int,
+    max_windows: int,
+    seed: int,
+    epoch: int = 0,
+) -> tuple[int, ...]:
+    """Selects a deterministic random bounded window subset for one epoch."""
+    if not sample_id.strip():
+        raise ValueError("sample_id must be non-empty.")
+    if window_count <= 0 or max_windows <= 0:
+        raise ValueError("window_count and max_windows must be positive.")
+    if epoch < 0:
+        raise ValueError("epoch must be non-negative.")
+    if window_count <= max_windows:
+        return tuple(range(window_count))
+    digest = hashlib.sha256(f"{seed}:{epoch}:{sample_id}".encode()).digest()
+    rng = random.Random(int.from_bytes(digest[:8], "big"))
+    return tuple(sorted(rng.sample(range(window_count), max_windows)))
+
+
+def sampling_contributions(
+    items: list[UtteranceSamplingItem],
+) -> dict[str, dict[str, float]]:
+    """Reports expected sample and duration contributions by corpus and class."""
+    item_by_id = {item.sample_id: item for item in items}
+    probabilities = utterance_sampling_distribution(items)
+    corpus: defaultdict[str, float] = defaultdict(float)
+    classes: defaultdict[str, float] = defaultdict(float)
+    duration: defaultdict[str, float] = defaultdict(float)
+    for row in probabilities:
+        corpus[row.corpus] += row.probability
+        classes[f"{row.corpus}:{row.label}"] += row.probability
+        seconds = item_by_id[row.sample_id].duration_seconds
+        if seconds is not None:
+            duration[row.corpus] += row.probability * seconds
+    return {
+        "corpus": dict(sorted(corpus.items())),
+        "class": dict(sorted(classes.items())),
+        "expected_duration_seconds": dict(sorted(duration.items())),
+    }
diff --git a/tests/suites/unit/heads/test_multitask_loss.py b/tests/suites/unit/heads/test_multitask_loss.py
new file mode 100644
index 0000000..29a2b08
--- /dev/null
+++ b/tests/suites/unit/heads/test_multitask_loss.py
@@ -0,0 +1,40 @@
+"""Tests for masked uncertainty-weighted multitask losses."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from ser._internal.heads.multitask_loss import MaskedUncertaintyWeightedLoss
+
+
+def test_missing_auxiliary_targets_contribute_no_loss_or_gradient() -> None:
+    """Unavailable auxiliary labels remain isolated from the primary objective."""
+    objective = MaskedUncertaintyWeightedLoss(("primary_emotion", "vad"))
+    primary = torch.tensor([1.0, 3.0], requires_grad=True)
+    auxiliary = torch.tensor([100.0, 200.0], requires_grad=True)
+
+    value = objective(
+        {"primary_emotion": primary, "vad": auxiliary},
+        {
+            "primary_emotion": torch.tensor([True, True]),
+            "vad": torch.tensor([False, False]),
+        },
+    )
+    value.backward()
+
+    assert value.item() == pytest.approx(2.0)
+    assert primary.grad is not None
+    assert auxiliary.grad is None
+    assert objective.log_variances["vad"].grad is None
+
+
+def test_loss_rejects_batches_without_any_available_target() -> None:
+    """Silent zero-loss batches fail closed."""
+    objective = MaskedUncertaintyWeightedLoss(("primary_emotion",))
+
+    with pytest.raises(ValueError, match="No available targets"):
+        objective(
+            {"primary_emotion": torch.tensor([1.0])},
+            {"primary_emotion": torch.tensor([False])},
+        )
diff --git a/tests/suites/unit/models/test_utterance_sampling.py b/tests/suites/unit/models/test_utterance_sampling.py
new file mode 100644
index 0000000..1a8819b
--- /dev/null
+++ b/tests/suites/unit/models/test_utterance_sampling.py
@@ -0,0 +1,48 @@
+"""Tests for corpus/class balanced utterance sampling."""
+
+from __future__ import annotations
+
+import math
+
+from ser._internal.models.utterance_sampling import (
+    UtteranceSamplingItem,
+    select_training_windows,
+    utterance_sampling_distribution,
+)
+
+
+def test_distribution_uses_sqrt_corpus_and_inverse_sqrt_class_weights() -> None:
+    """Expected hierarchical mass matches the declared sampling policy."""
+    items = [
+        UtteranceSamplingItem("a:happy:1", "a", "happy", 10),
+        UtteranceSamplingItem("a:happy:2", "a", "happy", 20),
+        UtteranceSamplingItem("a:sad:1", "a", "sad", 1),
+        UtteranceSamplingItem("b:sad:1", "b", "sad", 1),
+    ]
+    rows = utterance_sampling_distribution(items)
+    corpus_a = sum(row.probability for row in rows if row.corpus == "a")
+    corpus_b = sum(row.probability for row in rows if row.corpus == "b")
+    expected_a = math.sqrt(3) / (math.sqrt(3) + 1)
+
+    assert math.isclose(corpus_a, expected_a)
+    assert math.isclose(corpus_b, 1.0 - expected_a)
+    happy_mass = sum(row.probability for row in rows if row.corpus == "a" and row.label == "happy")
+    sad_mass = sum(row.probability for row in rows if row.corpus == "a" and row.label == "sad")
+    assert sad_mass > happy_mass
+
+
+def test_training_window_selection_is_bounded_seeded_and_epoch_varying() -> None:
+    """Long utterances never materialize an unbounded training-window contribution."""
+    first = select_training_windows(
+        sample_id="corpus:1", window_count=100, max_windows=4, seed=7, epoch=0
+    )
+    repeated = select_training_windows(
+        sample_id="corpus:1", window_count=100, max_windows=4, seed=7, epoch=0
+    )
+    next_epoch = select_training_windows(
+        sample_id="corpus:1", window_count=100, max_windows=4, seed=7, epoch=1
+    )
+
+    assert len(first) == 4
+    assert first == repeated
+    assert first != next_epoch