diff --git a/moonfish/config.py b/moonfish/config.py
index 5e2b227..eb8304c 100644
--- a/moonfish/config.py
+++ b/moonfish/config.py
@@ -22,3 +22,4 @@ class Config:
     syzygy_pieces: int
     checkmate_score: int = CHECKMATE_SCORE
     checkmate_threshold: int = CHECKMATE_THRESHOLD
+    nn_model_path: str | None = None
diff --git a/moonfish/engines/alpha_beta.py b/moonfish/engines/alpha_beta.py
index bc97538..fbf385f 100644
--- a/moonfish/engines/alpha_beta.py
+++ b/moonfish/engines/alpha_beta.py
@@ -1,3 +1,4 @@
+import time
 from multiprocessing.managers import DictProxy
 
 import chess.syzygy
@@ -15,6 +16,9 @@
 NEG_INF = float("-inf")
 NULL_MOVE = Move.null()
 
+# Maximum depth for iterative deepening time-based search
+MAX_DEPTH = 100
+
 
 class AlphaBeta:
     """
@@ -24,6 +28,8 @@ class AlphaBeta:
     def __init__(self, config: Config):
         self.config = config
         self.nodes: int = 0
+        self._stop_time: float = 0.0  # Deadline for time-managed search
+        self._time_abort: bool = False  # Set True when time runs out
 
         # Open Syzygy tablebase once at initialization (not on every eval)
         self.tablebase = None
@@ -98,6 +104,15 @@ def quiescence_search(
 
         self.nodes += 1
 
+        # Check time limit periodically (every 512 nodes)
+        if self._stop_time and (self.nodes & 511) == 0:
+            if time.perf_counter() >= self._stop_time:
+                self._time_abort = True
+
+        # If time is up, return stand-pat immediately
+        if self._time_abort:
+            return self.eval_board(board)
+
         if board.is_checkmate():
             return -self.config.checkmate_score
 
@@ -210,6 +225,15 @@ def negamax(
 
         self.nodes += 1
 
+        # Check time limit periodically (every 512 nodes)
+        if self._stop_time and (self.nodes & 511) == 0:
+            if time.perf_counter() >= self._stop_time:
+                self._time_abort = True
+
+        # If time is up, return immediately with a rough score
+        if self._time_abort:
+            return 0, None
+
         # check if board was already evaluated
         if cache_key in cache:
             return cache[cache_key]
@@ -311,6 +335,8 @@ def negamax(
 
     def search_move(self, board: Board) -> Move:
         self.nodes = 0
+        self._time_abort = False
+        self._stop_time = 0.0
         # create shared cache
         cache: CACHE_KEY = {}
 
@@ -319,3 +345,51 @@ def search_move(self, board: Board) -> Move:
         )[1]
         assert best_move is not None, "Best move from root should not be None"
         return best_move
+
+    def search_move_timed(self, board: Board, time_limit_s: float) -> Move:
+        """
+        Search using iterative deepening with a time limit.
+
+        Searches from depth 1 upward. If a depth completes within the time
+        limit, its result is saved. If time runs out mid-search, the result
+        from the last completed depth is used.
+
+        Arguments:
+            - board: chess board state
+            - time_limit_s: maximum time in seconds for the entire search
+
+        Returns:
+            - best_move: the best move found within the time limit
+        """
+        self.nodes = 0
+        self._time_abort = False
+        self._stop_time = time.perf_counter() + time_limit_s
+        cache: CACHE_KEY = {}
+
+        best_move = None
+
+        for depth in range(1, MAX_DEPTH + 1):
+            score, move = self.negamax(
+                board, depth, self.config.null_move, cache,
+            )
+
+            if self._time_abort:
+                # Time ran out during this depth — discard partial result
+                break
+
+            if move is not None:
+                best_move = move
+
+            # If time remaining is less than what we'd need for the next depth
+            # (rough heuristic: next depth takes ~4x longer), stop early
+            elapsed = time.perf_counter() - (self._stop_time - time_limit_s)
+            remaining = self._stop_time - time.perf_counter()
+            if remaining < elapsed * 3:
+                break
+
+        self._stop_time = 0.0
+        self._time_abort = False
+
+        if best_move is None:
+            best_move = self.random_move(board)
+        return best_move
diff --git a/moonfish/engines/nn_engine.py b/moonfish/engines/nn_engine.py
new file mode 100644
index 0000000..69eff48
--- /dev/null
+++ b/moonfish/engines/nn_engine.py
@@ -0,0 +1,281 @@
+"""
+NNUE chess engine with accumulator state management.
+
+Uses alpha-beta search with an NNUE evaluator. Maintains accumulator state
+during search by saving/restoring before push/pop, enabling incremental
+updates instead of full recomputation at every node.
+"""
+
+from multiprocessing.managers import DictProxy
+
+import chess
+from chess import Board, Move
+
+from moonfish.config import Config
+from moonfish.engines.alpha_beta import AlphaBeta, CACHE_KEY, INF, NEG_INF, NULL_MOVE
+from moonfish.evaluation.classical import ClassicalEvaluator
+from moonfish.evaluation.nn import NNUEEvaluator
+from moonfish.move_ordering import organize_moves, organize_moves_quiescence
+from moonfish.psqt import count_pieces
+
+
+class NNEngine(AlphaBeta):
+    """
+    Chess engine using NNUE evaluation with incremental accumulator updates.
+
+    Overrides the search methods from AlphaBeta to save/restore NNUE
+    accumulator state around board.push/pop, enabling efficient incremental
+    updates during the search tree traversal.
+    """
+
+    def __init__(
+        self,
+        config: Config,
+        evaluator: NNUEEvaluator | ClassicalEvaluator | None = None,
+    ):
+        super().__init__(config)
+        self.evaluator = evaluator or ClassicalEvaluator()
+        self._use_nnue = isinstance(self.evaluator, NNUEEvaluator)
+
+    def eval_board(self, board: Board) -> float:
+        """Evaluate board using the NNUE evaluator (or classical fallback)."""
+        if self.tablebase is not None:
+            pieces = sum(count_pieces(board))
+            if pieces <= self.config.syzygy_pieces:
+                try:
+                    dtz = self.tablebase.probe_dtz(board)
+                    return dtz
+                except (chess.syzygy.MissingTableError, KeyError):
+                    pass
+
+        return self.evaluator.evaluate(board)
+
+    def _push_move(self, board: Board, move: Move) -> tuple | None:
+        """Save accumulator state, apply incremental update, push move."""
+        saved = None
+        if self._use_nnue:
+            nnue = self.evaluator
+            saved = nnue.save_accumulators()
+            if move != NULL_MOVE:
+                nnue.update_move(board, move)
+        board.push(move)
+        return saved
+
+    def _pop_move(self, board: Board, saved: tuple | None) -> None:
+        """Pop move and restore accumulator state."""
+        board.pop()
+        if saved is not None and self._use_nnue:
+            self.evaluator.restore_accumulators(saved)
+
+    def quiescence_search(
+        self,
+        board: Board,
+        depth: int,
+        alpha: float,
+        beta: float,
+    ) -> float:
+        """Quiescence search with accumulator save/restore."""
+        import time
+
+        in_check = board.is_check()
+        self.nodes += 1
+
+        if self._stop_time and (self.nodes & 511) == 0:
+            if time.perf_counter() >= self._stop_time:
+                self._time_abort = True
+        if self._time_abort:
+            return self.eval_board(board)
+
+        if board.is_checkmate():
+            return -self.config.checkmate_score
+        if board.is_stalemate():
+            return 0
+        if board.is_fifty_moves() or board.is_insufficient_material():
+            return 0
+
+        stand_pat = self.eval_board(board)
+
+        if in_check:
+            if depth <= 0:
+                return stand_pat
+            best_score = NEG_INF
+            moves = list(board.legal_moves)
+        else:
+            if depth <= 0:
+                return stand_pat
+            if stand_pat >= beta:
+                return beta
+            best_score = stand_pat
+            alpha = max(alpha, stand_pat)
+            moves = organize_moves_quiescence(board)
+
+        for move in moves:
+            saved = self._push_move(board, move)
+
+            if board.is_repetition(2):
+                score = 0.0
+            else:
+                score = -self.quiescence_search(board, depth - 1, -beta, -alpha)
+
+            self._pop_move(board, saved)
+
+            if score > best_score:
+                best_score = score
+            if score >= beta:
+                return beta
+            alpha = max(alpha, score)
+
+        return best_score
+
+    def negamax(
+        self,
+        board: Board,
+        depth: int,
+        null_move: bool,
+        cache: DictProxy | CACHE_KEY,
+        alpha: float = NEG_INF,
+        beta: float = INF,
+    ) -> tuple[float | int, Move | None]:
+        """Negamax search with accumulator save/restore."""
+        import time
+
+        cache_key = (board._transposition_key(), depth, null_move, alpha, beta)
+        self.nodes += 1
+
+        if self._stop_time and (self.nodes & 511) == 0:
+            if time.perf_counter() >= self._stop_time:
+                self._time_abort = True
+        if self._time_abort:
+            return 0, None
+
+        if cache_key in cache:
+            return cache[cache_key]
+
+        if board.is_checkmate():
+            cache[cache_key] = (-self.config.checkmate_score, None)
+            return (-self.config.checkmate_score, None)
+        if board.is_stalemate():
+            cache[cache_key] = (0, None)
+            return (0, None)
+
+        if depth <= 0:
+            board_score = self.quiescence_search(
+                board, self.config.quiescence_search_depth, alpha, beta
+            )
+            cache[cache_key] = (board_score, None)
+            return board_score, None
+
+        # Null move pruning
+        if (
+            self.config.null_move
+            and depth >= (self.config.null_move_r + 1)
+            and not board.is_check()
+        ):
+            board_score = self.eval_board(board)
+            if board_score >= beta:
+                saved = self._push_move(board, NULL_MOVE)
+                board_score = -self.negamax(
+                    board,
+                    depth - 1 - self.config.null_move_r,
+                    False,
+                    cache,
+                    -beta,
+                    -beta + 1,
+                )[0]
+                self._pop_move(board, saved)
+                if board_score >= beta:
+                    cache[cache_key] = (beta, None)
+                    return beta, None
+
+        best_move = None
+        best_score = NEG_INF
+        moves = organize_moves(board)
+
+        for move in moves:
+            saved = self._push_move(board, move)
+
+            board_score = -self.negamax(
+                board, depth - 1, null_move, cache, -beta, -alpha
+            )[0]
+            if board_score > self.config.checkmate_threshold:
+                board_score -= 1
+            if board_score < -self.config.checkmate_threshold:
+                board_score += 1
+
+            self._pop_move(board, saved)
+
+            if board_score >= beta:
+                cache[cache_key] = (board_score, move)
+                return board_score, move
+
+            if board_score > best_score:
+                best_score = board_score
+                best_move = move
+
+            alpha = max(alpha, board_score)
+            if alpha >= beta:
+                break
+
+        if not best_move:
+            best_move = self.random_move(board)
+
+        cache[cache_key] = (best_score, best_move)
+        return best_score, best_move
+
+    def search_move(self, board: Board) -> Move:
+        """Search for the best move, initializing NNUE accumulators first."""
+        self.nodes = 0
+        self._time_abort = False
+        self._stop_time = 0.0
+
+        if self._use_nnue:
+            self.evaluator.reset(board)
+        elif hasattr(self.evaluator, "reset"):
+            self.evaluator.reset()
+
+        cache: CACHE_KEY = {}
+        best_move = self.negamax(
+            board, self.config.negamax_depth, self.config.null_move, cache
+        )[1]
+        assert best_move is not None, "Best move from root should not be None"
+        return best_move
+
+    def search_move_timed(self, board: Board, time_limit_s: float) -> Move:
+        """Search with iterative deepening + time limit, maintaining accumulators."""
+        import time
+
+        self.nodes = 0
+        self._time_abort = False
+        self._stop_time = time.perf_counter() + time_limit_s
+
+        if self._use_nnue:
+            self.evaluator.reset(board)
+        elif hasattr(self.evaluator, "reset"):
+            self.evaluator.reset()
+
+        cache: CACHE_KEY = {}
+        best_move = None
+        MAX_DEPTH = 100
+
+        for depth in range(1, MAX_DEPTH + 1):
+            score, move = self.negamax(
+                board, depth, self.config.null_move, cache
+            )
+
+            if self._time_abort:
+                break
+
+            if move is not None:
+                best_move = move
+
+            elapsed = time.perf_counter() - (self._stop_time - time_limit_s)
+            remaining = self._stop_time - time.perf_counter()
+            if remaining < elapsed * 3:
+                break
+
+        self._stop_time = 0.0
+        self._time_abort = False
+
+        if best_move is None:
+            best_move = self.random_move(board)
+        return best_move
diff --git a/moonfish/evaluation/__init__.py b/moonfish/evaluation/__init__.py
new file mode 100644
index 0000000..29e54b4
--- /dev/null
+++ b/moonfish/evaluation/__init__.py
@@ -0,0 +1,5 @@
+from moonfish.evaluation.base import Evaluator
+from moonfish.evaluation.classical import ClassicalEvaluator
+from moonfish.evaluation.nn import NNUEEvaluator
+
+__all__ = ["Evaluator", "ClassicalEvaluator", "NNUEEvaluator"]
diff --git a/moonfish/evaluation/base.py b/moonfish/evaluation/base.py
new file mode 100644
index 0000000..f7804fd
--- /dev/null
+++ b/moonfish/evaluation/base.py
@@ -0,0 +1,49 @@
+"""
+Base evaluator protocol.
+
+Any evaluation function (classical, NNUE, transformer, LLM) should implement
+this protocol to be usable with the alpha-beta search engine.
+"""
+
+from typing import Protocol, runtime_checkable
+
+from chess import Board
+
+
+@runtime_checkable
+class Evaluator(Protocol):
+    """
+    Protocol for board evaluation functions.
+
+    Implementations can range from simple piece-square tables to neural networks
+    or even LLM-based evaluators. The engine will call `evaluate()` at leaf nodes
+    of the search tree and in quiescence search.
+
+    The returned score should be from the perspective of the side to move:
+    - Positive = good for the side to move
+    - Negative = bad for the side to move
+    - 0 = roughly equal
+
+    Scores are in centipawns (100 = 1 pawn advantage).
+    """
+
+    def evaluate(self, board: Board) -> float:
+        """
+        Evaluate the given board position.
+
+        Args:
+            board: The chess position to evaluate.
+
+        Returns:
+            Score in centipawns from the side-to-move's perspective.
+        """
+        ...
+
+    def reset(self) -> None:
+        """
+        Reset any internal state (e.g., caches, accumulators).
+
+        Called at the start of each new search. Implementations that
+        don't maintain state can make this a no-op.
+        """
+        ...
diff --git a/moonfish/evaluation/classical.py b/moonfish/evaluation/classical.py
new file mode 100644
index 0000000..4120b0a
--- /dev/null
+++ b/moonfish/evaluation/classical.py
@@ -0,0 +1,25 @@
+"""
+Classical evaluator wrapping the existing PeSTO evaluation.
+
+This is the default evaluator used by the engine. It provides a baseline
+for comparing against neural network evaluators.
+"""
+
+from chess import Board
+
+from moonfish.psqt import BOARD_EVALUATION_CACHE, board_evaluation
+
+
+class ClassicalEvaluator:
+    """
+    Classical evaluation based on PeSTO piece-square tables with
+    tapered midgame/endgame scoring.
+    """
+
+    def evaluate(self, board: Board) -> float:
+        """Evaluate using PeSTO piece-square tables."""
+        return board_evaluation(board)
+
+    def reset(self) -> None:
+        """Clear the evaluation cache between searches."""
+        BOARD_EVALUATION_CACHE.clear()
diff --git a/moonfish/evaluation/nn.py b/moonfish/evaluation/nn.py
new file mode 100644
index 0000000..9b3e888
--- /dev/null
+++ b/moonfish/evaluation/nn.py
@@ -0,0 +1,182 @@
+"""
+NNUE evaluator with incremental accumulator updates.
+
+Implements a dual-perspective efficiently updatable neural network (NNUE)
+following the architecture pioneered by Stockfish. The network uses:
+
+- 768 sparse binary input features (6 piece types x 2 colors x 64 squares)
+- Dual-perspective feature transformer with shared weights
+- SCReLU activation (squared clipped ReLU)
+- Single output neuron
+
+Accumulators are updated incrementally during search: each move only
+changes 2-4 input features, so we add/subtract weight columns rather
+than recomputing the full matrix multiply.
+"""
+
+import numpy as np
+from chess import Board
+
+import chess
+
+# Feature encoding: 768 binary features
+# Index = piece_type_index * 128 + color * 64 + square
+# piece_type_index: PAWN=0, KNIGHT=1, BISHOP=2, ROOK=3, QUEEN=4, KING=5
+# color: WHITE=0, BLACK=1
+# square: 0..63 (a1=0, h8=63)
+NUM_FEATURES = 768
+HIDDEN_SIZE = 128
+
+
+def feature_index(piece_type: int, color: bool, square: int) -> int:
+    """Compute the feature index for a piece on a square (white perspective)."""
+    return (piece_type - 1) * 128 + int(color) * 64 + square
+
+
+def feature_index_flipped(piece_type: int, color: bool, square: int) -> int:
+    """Compute the feature index from black's perspective (flip colors and squares)."""
+    flipped_color = not color
+    flipped_square = square ^ 56  # Vertical flip: rank 0 <-> rank 7
+    return (piece_type - 1) * 128 + int(flipped_color) * 64 + flipped_square
+
+
+class NNUEEvaluator:
+    """
+    NNUE evaluator with dual-perspective accumulators and incremental updates.
+
+    Architecture:
+        Input (768 sparse binary) -> Linear(768, 128) shared weights
+        Two accumulators: white perspective + black perspective
+        SCReLU on each -> concat(128+128=256) -> Linear(256, 1) -> output
+
+    The accumulator state can be saved and restored during search to avoid
+    full recomputation after board.pop().
+    """
+
+    def __init__(self, weights_path: str):
+        data = np.load(weights_path)
+        self.w1 = data["w1"].astype(np.float32)  # (768, 128)
+        self.b1 = data["b1"].astype(np.float32)  # (128,)
+        self.w2 = data["w2"].astype(np.float32)  # (256,) or (256, 1)
+        self.b2 = data["b2"].astype(np.float32)  # (1,) or scalar
+
+        # Flatten w2 to 1D for dot product
+        self.w2 = self.w2.ravel()
+        self.b2 = float(self.b2.ravel()[0])
+
+        # Accumulators: will be set by reset()
+        self.white_acc = np.copy(self.b1)
+        self.black_acc = np.copy(self.b1)
+
+    def reset(self, board: Board | None = None) -> None:
+        """Full recompute of accumulators from board position."""
+        self.white_acc = np.copy(self.b1)
+        self.black_acc = np.copy(self.b1)
+        if board is not None:
+            for sq, piece in board.piece_map().items():
+                w_idx = feature_index(piece.piece_type, piece.color, sq)
+                b_idx = feature_index_flipped(piece.piece_type, piece.color, sq)
+                self.white_acc += self.w1[w_idx]
+                self.black_acc += self.w1[b_idx]
+
+    def save_accumulators(self) -> tuple[np.ndarray, np.ndarray]:
+        """Save current accumulator state (for search stack)."""
+        return np.copy(self.white_acc), np.copy(self.black_acc)
+
+    def restore_accumulators(
+        self, saved: tuple[np.ndarray, np.ndarray]
+    ) -> None:
+        """Restore accumulator state from saved copy."""
+        self.white_acc = saved[0]
+        self.black_acc = saved[1]
+
+    def _add_piece(self, piece_type: int, color: bool, square: int) -> None:
+        """Add a piece to accumulators."""
+        w_idx = feature_index(piece_type, color, square)
+        b_idx = feature_index_flipped(piece_type, color, square)
+        self.white_acc += self.w1[w_idx]
+        self.black_acc += self.w1[b_idx]
+
+    def _remove_piece(self, piece_type: int, color: bool, square: int) -> None:
+        """Remove a piece from accumulators."""
+        w_idx = feature_index(piece_type, color, square)
+        b_idx = feature_index_flipped(piece_type, color, square)
+        self.white_acc -= self.w1[w_idx]
+        self.black_acc -= self.w1[b_idx]
+
+    def update_move(self, board: Board, move: chess.Move) -> None:
+        """
+        Incrementally update accumulators for a move.
+
+        Must be called BEFORE board.push(move). The board state is used
+        to determine captures, castling, en passant, and promotions.
+
+        Typically 2 feature changes (quiet move), 4 for captures/castling/promotion.
+        """
+        from_sq = move.from_square
+        to_sq = move.to_square
+        piece = board.piece_at(from_sq)
+        if piece is None:
+            return
+
+        moving_type = piece.piece_type
+        moving_color = piece.color
+
+        # Handle capture (remove captured piece)
+        if board.is_en_passant(move):
+            # En passant: captured pawn is on a different square
+            if moving_color == chess.WHITE:
+                capture_sq = to_sq - 8
+            else:
+                capture_sq = to_sq + 8
+            self._remove_piece(chess.PAWN, not moving_color, capture_sq)
+        elif board.is_capture(move):
+            captured = board.piece_at(to_sq)
+            if captured is not None:
+                self._remove_piece(captured.piece_type, captured.color, to_sq)
+
+        # Remove piece from origin square
+        self._remove_piece(moving_type, moving_color, from_sq)
+
+        # Handle promotion
+        if move.promotion is not None:
+            # Add promoted piece to destination
+            self._add_piece(move.promotion, moving_color, to_sq)
+        else:
+            # Add piece to destination
+            self._add_piece(moving_type, moving_color, to_sq)
+
+        # Handle castling: also move the rook
+        if board.is_castling(move):
+            if to_sq > from_sq:
+                # Kingside
+                rook_from = chess.square(7, chess.square_rank(from_sq))
+                rook_to = chess.square(5, chess.square_rank(from_sq))
+            else:
+                # Queenside
+                rook_from = chess.square(0, chess.square_rank(from_sq))
+                rook_to = chess.square(3, chess.square_rank(from_sq))
+            self._remove_piece(chess.ROOK, moving_color, rook_from)
+            self._add_piece(chess.ROOK, moving_color, rook_to)
+
+    def evaluate(self, board: Board) -> float:
+        """
+        Forward pass with SCReLU activation.
+
+        Returns score from the side-to-move's perspective.
+        """
+        if board.turn == chess.WHITE:
+            stm = self.white_acc
+            nstm = self.black_acc
+        else:
+            stm = self.black_acc
+            nstm = self.white_acc
+
+        # SCReLU: clamp(x, 0, 1)^2
+        stm_out = np.clip(stm, 0.0, 1.0)
+        stm_out = stm_out * stm_out
+        nstm_out = np.clip(nstm, 0.0, 1.0)
+        nstm_out = nstm_out * nstm_out
+
+        hidden = np.concatenate([stm_out, nstm_out])
+        return float(np.dot(hidden, self.w2) + self.b2)
diff --git a/moonfish/helper.py b/moonfish/helper.py
index d7fd04d..2b42126 100644
--- a/moonfish/helper.py
+++ b/moonfish/helper.py
@@ -10,6 +10,7 @@
 from moonfish.engines.l1p_alpha_beta import Layer1ParallelAlphaBeta
 from moonfish.engines.l2p_alpha_beta import Layer2ParallelAlphaBeta
 from moonfish.engines.lazy_smp import LazySMP
+from moonfish.engines.nn_engine import NNEngine
 from moonfish.engines.random import RandomEngine
 
 
@@ -21,6 +22,7 @@ class Algorithm(Enum):
     parallel_alpha_beta_layer_2 = "parallel_alpha_beta_layer_2"
     lazy_smp = "lazy_smp"
     random = "random"
+    nn = "nn"
 
 
 def get_engine(config: Config):
@@ -45,9 +47,36 @@ def get_engine(config: Config):
         return LazySMP(config)
     elif algorithm is Algorithm.random:
         return RandomEngine(config)
+    elif algorithm is Algorithm.nn:
+        return _create_nn_engine(config)
     raise Exception("algorithm not supported")
 
 
+def _default_nn_model_path() -> str | None:
+    """Resolve the default NNUE model path (bundled with the package)."""
+    model = Path(__file__).resolve().parent / "models" / "nnue_v1.npz"
+    if model.is_file():
+        return str(model)
+    return None
+
+
+def _create_nn_engine(config: Config) -> NNEngine:
+    """
+    Create an NN engine with the configured evaluator.
+
+    If nn_model_path is set, loads the model from that path.
+    Otherwise, looks for the bundled default model.
+    Falls back to classical evaluation if no model is found.
+    """
+    model_path = config.nn_model_path or _default_nn_model_path()
+    evaluator = None
+    if model_path:
+        from moonfish.evaluation.nn import NNUEEvaluator
+
+        evaluator = NNUEEvaluator(model_path)
+    return NNEngine(config, evaluator=evaluator)
+
+
 def _opening_book_path() -> str:
     """
     Resolve the opening book path, allowing overrides for CI or custom installs.
diff --git a/moonfish/main.py b/moonfish/main.py
index a2a36cf..ed5291e 100644
--- a/moonfish/main.py
+++ b/moonfish/main.py
@@ -62,6 +62,12 @@ def run(config: Config):
     help="Remaining pieces to use syzygy endgame tablebases.",
     default=5,
 )
+@click.option(
+    "--nn-model-path",
+    type=str,
+    help="Path to NNUE model weights (.npz) for the nn algorithm.",
+    default=None,
+)
 def main(
     mode: str,
     algorithm: str,
@@ -71,6 +77,7 @@ def main(
     quiescence_search_depth: int,
     syzygy_path: str | None,
     syzygy_pieces: int,
+    nn_model_path: str | None,
 ):
     """
     Starts the engine according to the options provided.
@@ -84,6 +91,7 @@ def main(
         quiescence_search_depth=quiescence_search_depth,
         syzygy_path=syzygy_path,
         syzygy_pieces=syzygy_pieces,
+        nn_model_path=nn_model_path,
     )
     run(config)
 
diff --git a/moonfish/mode/uci.py b/moonfish/mode/uci.py
index d147b9d..cf304ec 100644
--- a/moonfish/mode/uci.py
+++ b/moonfish/mode/uci.py
@@ -1,12 +1,68 @@
 import sys
 
-from chess import Board, STARTING_FEN
+from chess import WHITE, Board, STARTING_FEN
 from moonfish.config import Config
 from moonfish.helper import find_best_move, get_engine
 
 # UCI based on Sunfish Engine: https://github.com/thomasahle/sunfish/blob/master/uci.py
 
 
+def _parse_go_params(params: list[str]) -> dict[str, int]:
+    """Parse 'go' command parameters into a dict."""
+    result: dict[str, int] = {}
+    i = 0
+    while i < len(params):
+        key = params[i]
+        if key in ("wtime", "btime", "winc", "binc", "movetime", "movestogo", "depth"):
+            if i + 1 < len(params):
+                try:
+                    result[key] = int(params[i + 1])
+                except ValueError:
+                    pass
+                i += 2
+                continue
+        elif key == "infinite":
+            result["infinite"] = 1
+        i += 1
+    return result
+
+
+def _calculate_time_limit(
+    go_params: dict[str, int], side_to_move_is_white: bool
+) -> float | None:
+    """
+    Calculate time limit in seconds from UCI go parameters.
+    Returns None if the engine should use fixed depth.
+    """
+    # Fixed time per move (movetime)
+    if "movetime" in go_params:
+        # Use movetime minus a 1-second safety margin for Python/GC overhead
+        movetime_ms = go_params["movetime"]
+        return max((movetime_ms - 1000) / 1000.0, movetime_ms / 1000.0 * 0.5)
+
+    # Clock-based time management
+    time_key = "wtime" if side_to_move_is_white else "btime"
+    inc_key = "winc" if side_to_move_is_white else "binc"
+
+    if time_key in go_params:
+        remaining_ms = go_params[time_key]
+        increment_ms = go_params.get(inc_key, 0)
+        moves_to_go = go_params.get("movestogo", 30)
+
+        # Allocate time: remaining / moves_to_go + most of the increment
+        time_for_move_ms = remaining_ms / moves_to_go + increment_ms * 0.8
+
+        # Don't use more than 25% of remaining time on one move
+        time_for_move_ms = min(time_for_move_ms, remaining_ms * 0.25)
+
+        # Safety margin for Python/GC overhead
+        time_for_move_ms = max(time_for_move_ms - 500, 10)
+
+        return time_for_move_ms / 1000.0
+
+    return None
+
+
 def main(config: Config):
     """
     Start the command line user interface (UCI based).
@@ -66,8 +122,22 @@ def main(config: Config):
                 board.push_uci(move)
 
         elif uci_command.startswith("go"):
-            best_move = find_best_move(
-                board=board,
-                engine=engine,
+            go_params = _parse_go_params(uci_parameters[1:])
+
+            # If depth is specified in go command, use it
+            if "depth" in go_params:
+                config.negamax_depth = go_params["depth"]
+
+            # Calculate time limit from go parameters
+            time_limit = _calculate_time_limit(
+                go_params, board.turn == WHITE
             )
+
+            if time_limit is not None and hasattr(engine, "search_move_timed"):
+                best_move = engine.search_move_timed(board, time_limit)
+            else:
+                best_move = find_best_move(
+                    board=board,
+                    engine=engine,
+                )
             print(f"bestmove {best_move}")
diff --git a/moonfish/models/nnue_v1.npz b/moonfish/models/nnue_v1.npz
new file mode 100644
index 0000000..86d8850
Binary files /dev/null and b/moonfish/models/nnue_v1.npz differ
diff --git a/pyproject.toml b/pyproject.toml
index 0fbf7c5..7aacddd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ authors = [
 dependencies = [
   "chess",
   "click",
+  "numpy",
   "Flask",
   "Flask-Cors",
   "parameterized",
@@ -31,6 +32,9 @@ dependencies = [
 moonfish = "moonfish.main:main"
 
 [project.optional-dependencies]
+nn = [
+  "torch>=2.0.0",
+]
 rl = [
   "fastapi>=0.100.0",
   "uvicorn[standard]>=0.23.0",
@@ -39,18 +43,20 @@ rl = [
 ]
 
 [tool.setuptools]
-packages = ["moonfish", "moonfish.rl", "moonfish.rl.server", "moonfish.rl.examples"]
+packages = ["moonfish", "moonfish.engines", "moonfish.evaluation", "moonfish.rl", "moonfish.rl.server", "moonfish.rl.examples"]
 
 [tool.setuptools_scm]
 version_scheme = "post-release"
 local_scheme = "no-local-version"
 
 [tool.setuptools.package-data]
-"moonfish" = ["py.typed"]
+"moonfish" = ["py.typed", "models/*.npz"]
 
 [[tool.mypy.overrides]]
 module = [
     "parameterized.*",
+    "torch",
+    "torch.*",
     "httpx",
     "httpx.*",
     "fastapi",
diff --git a/scripts/generate_training_data.py b/scripts/generate_training_data.py
new file mode 100644
index 0000000..0f1b614
--- /dev/null
+++ b/scripts/generate_training_data.py
@@ -0,0 +1,332 @@
+#!/usr/bin/env python3
+"""
+Generate NNUE training data using Stockfish evaluations.
+
+Produces labeled chess positions for training the NNUE evaluation network.
+Each position is labeled with Stockfish's evaluation at the specified depth
+and its WDL (Win/Draw/Loss) probabilities.
+
+Requires: python-chess, numpy, and Stockfish binary in PATH or via --stockfish.
+
+Usage:
+    uv run python scripts/generate_training_data.py \
+        --num-positions 1000000 \
+        --depth 8 \
+        --output data/training_data.npz \
+        --workers 4
+"""
+
+import argparse
+import multiprocessing
+import os
+import random
+import sys
+import time
+from pathlib import Path
+
+import chess
+import chess.engine
+import numpy as np
+
+
+# 48 bench positions from Stockfish (same as moonfish bench)
+SEED_POSITIONS = [
+    "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
+    "r3k2r/p1ppqpb1/bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPBBPPP/R3K2R w KQkq - 0 10",
+    "8/2p5/3p4/KP5r/1R3p1k/8/4P1P1/8 w - - 0 11",
+    "4rrk1/pp1n3p/3q2pQ/2p1pb2/2PP4/2P3N1/P2B2PP/4RRK1 b - - 7 19",
+    "r1bq1r1k/1pp1n1pp/1p1p4/4p2Q/4Pp2/1BNP4/PPP2PPP/3R1RK1 w - - 2 14",
+    "r3r1k1/2p2ppp/p1p1bn2/8/1q2P3/2NPQN2/PPP3PP/R4RK1 b - - 2 15",
+    "r1bbk1nr/pp3p1p/2n5/1N4p1/2Np1B2/8/PPP2PPP/2KR1B1R w kq - 0 13",
+    "r1bq1rk1/ppp1nppp/4n3/3p3Q/3P4/1BP1B3/PP1N2PP/R4RK1 w - - 1 16",
+    "4r1k1/r1q2ppp/ppp2n2/4P3/5Rb1/1N1BQ3/PPP3PP/R5K1 w - - 1 17",
+    "2rqkb1r/ppp2p2/2npb1p1/1N1Nn2p/2P1PP2/8/PP2B1PP/R1BQK2R b KQ - 0 11",
+    "r1bq1r1k/b1p1npp1/p2p3p/1p6/3PP3/1B2NN2/PP3PPP/R2Q1RK1 w - - 1 16",
+    "3r1rk1/p5pp/bpp1pp2/8/q1PP1P2/b3P3/P2NQRPP/1R2B1K1 b - - 6 22",
+    "r1q2rk1/2p1bppp/2Pp4/p6b/Q1PNp3/4B3/PP1R1PPP/2K4R w - - 2 18",
+    "4k2r/1pb2ppp/1p2p3/1R1p4/3P4/2r1PN2/P4PPP/1R4K1 b - - 3 22",
+    "3q2k1/pb3p1p/4pbp1/2r5/PpN2N2/1P2P2P/5PP1/Q2R2K1 b - - 4 26",
+    "6k1/6p1/6Pp/ppp5/3pn2P/1P3K2/1PP2P2/3N4 b - - 0 1",
+    "3b4/5kp1/1p1p1p1p/pP1PpP1P/P1P1P3/3KN3/8/8 w - - 0 1",
+    "8/6pk/1p6/8/PP3p1p/5P2/4KP1q/3Q4 w - - 0 1",
+    "7k/3p2pp/4q3/8/4Q3/5Kp1/P6b/8 w - - 0 1",
+    "8/2p5/8/2kPKp1p/2p4P/2P5/3P4/8 w - - 0 1",
+    "8/1p3pp1/7p/5P1P/2k3P1/8/2K2P2/8 w - - 0 1",
+    "8/pp2r1k1/2p1p3/3pP2p/1P1P1P1P/P5KR/8/8 w - - 0 1",
+    "8/3p4/p1bk3p/Pp6/1Kp1PpPp/2P2P1P/2P5/5B2 b - - 0 1",
+    "5k2/7R/4P2p/5K2/p1r2P1p/8/8/8 b - - 0 1",
+    "6k1/6p1/P6p/r1N5/5p2/7P/1b3PP1/4R1K1 w - - 0 1",
+    "1r3k2/4q3/2Pp3b/3Bp3/2Q2p2/1p1P2P1/1P2KP2/3N4 w - - 0 1",
+    "6k1/4pp1p/3p2p1/P1pPb3/R7/1r2P1PP/3B1P2/6K1 w - - 0 1",
+    "8/3p3B/5p2/5P2/p7/PP5b/k7/6K1 w - - 0 1",
+    "5rk1/q6p/2p3bR/1pPp1rP1/1P1Pp3/P3B1Q1/1K3P2/R7 w - - 93 90",
+    "4rrk1/1p1nq3/p7/2p1P1pp/3P2bp/3Q1Bn1/PPPB4/1K2R1NR w - - 40 21",
+    "r3k2r/3nnpbp/q2pp1p1/p7/Pp1PPPP1/4BNN1/1P5P/R2Q1RK1 w kq - 0 16",
+    "3Qb1k1/1r2ppb1/pN1n2q1/Pp1Pp1Pr/4P2p/4BP2/4B1R1/1R5K b - - 11 40",
+    "4k3/3q1r2/1N2r1b1/3ppN2/2nPP3/1B1R2n1/2R1Q3/3K4 w - - 5 1",
+    "8/8/8/8/5kp1/P7/8/1K1N4 w - - 0 1",
+    "8/8/8/5N2/8/p7/8/2NK3k w - - 0 1",
+    "8/3k4/8/8/8/4B3/4KB2/2B5 w - - 0 1",
+    "8/8/1P6/5pr1/8/4R3/7k/2K5 w - - 0 1",
+    "8/2p4P/8/kr6/6R1/8/8/1K6 w - - 0 1",
+    "8/8/3P3k/8/1p6/8/1P6/1K3n2 b - - 0 1",
+    "8/R7/2q5/8/6k1/8/1P5p/K6R w - - 0 124",
+    "6k1/3b3r/1p1p4/p1n2p2/1PPNpP1q/P3Q1p1/1R1RB1P1/5K2 b - - 0 1",
+    "r2r1n2/pp2bk2/2p1p2p/3q4/3PN1QP/2P3R1/P4PP1/5RK1 w - - 0 1",
+]
+
+
+def feature_index(piece_type: int, color: bool, square: int) -> int:
+    """Compute NNUE feature index for a piece (white perspective)."""
+    return (piece_type - 1) * 128 + int(color) * 64 + square
+
+
+def encode_position(board: chess.Board) -> list[int]:
+    """Return active feature indices for a position (white perspective)."""
+    features = []
+    for sq, piece in board.piece_map().items():
+        features.append(feature_index(piece.piece_type, piece.color, sq))
+    return sorted(features)
+
+
+def generate_positions_from_seed(
+    seed_fen: str,
+    stockfish_path: str,
+    num_games: int,
+    depth: int,
+    max_positions_per_seed: int,
+) -> list[tuple[list[int], int, float]]:
+    """
+    Generate positions from a seed by playing random/Stockfish games.
+
+    Returns list of (feature_indices, score_cp, wdl_win_prob) tuples.
+    """
+    results = []
+    seen_fens = set()
+
+    try:
+        engine = chess.engine.SimpleEngine.popen_uci(stockfish_path)
+    except Exception as e:
+        print(f"Error opening Stockfish: {e}", file=sys.stderr)
+        return results
+
+    try:
+        for game_idx in range(num_games):
+            if len(results) >= max_positions_per_seed:
+                break
+
+            board = chess.Board(seed_fen)
+
+            # Play random opening moves (5-10 moves)
+            num_random = random.randint(5, 10)
+            for _ in range(num_random):
+                legal = list(board.legal_moves)
+                if not legal:
+                    break
+                board.push(random.choice(legal))
+
+            # Play a game mixing Stockfish moves with occasional random moves
+            ply = 0
+            next_random_ply = random.randint(8, 12)
+
+            while not board.is_game_over() and ply < 300:
+                ply += 1
+
+                # Should we add this position to training data?
+                fen_key = board.board_fen() + " " + ("w" if board.turn else "b")
+                if (
+                    ply >= 1
+                    and not board.is_check()
+                    and fen_key not in seen_fens
+                ):
+                    try:
+                        info = engine.analyse(
+                            board,
+                            chess.engine.Limit(depth=depth),
+                        )
+                        score = info["score"].white()
+
+                        # Skip mate scores
+                        if score.is_mate():
+                            pass
+                        else:
+                            cp = score.score()
+                            if cp is not None and abs(cp) <= 10000:
+                                # Get WDL if available
+                                wdl = info.get("wdl")
+                                if wdl is not None:
+                                    # WDL is (wins, draws, losses) per mille from side to move
+                                    w, d, l = wdl
+                                    # Convert to win probability from white's perspective
+                                    if board.turn == chess.WHITE:
+                                        win_prob = w / 1000.0
+                                    else:
+                                        win_prob = l / 1000.0
+                                else:
+                                    # Estimate from score using sigmoid
+                                    win_prob = 1.0 / (1.0 + 10.0 ** (-cp / 400.0))
+
+                                features = encode_position(board)
+                                results.append((features, cp, win_prob))
+                                seen_fens.add(fen_key)
+
+                                if len(results) >= max_positions_per_seed:
+                                    break
+                    except Exception:
+                        pass
+
+                # Make next move
+                legal = list(board.legal_moves)
+                if not legal:
+                    break
+
+                if ply >= next_random_ply:
+                    # Inject a random move for diversity
+                    board.push(random.choice(legal))
+                    next_random_ply = ply + random.randint(8, 12)
+                else:
+                    # Use Stockfish at low depth for speed
+                    try:
+                        result = engine.play(
+                            board,
+                            chess.engine.Limit(depth=random.randint(1, 4)),
+                        )
+                        board.push(result.move)
+                    except Exception:
+                        board.push(random.choice(legal))
+    finally:
+        engine.quit()
+
+    return results
+
+
+def worker_fn(args):
+    """Worker function for multiprocessing."""
+    seed_fens, stockfish_path, games_per_seed, depth, positions_per_seed = args
+    all_results = []
+    for fen in seed_fens:
+        results = generate_positions_from_seed(
+            fen, stockfish_path, games_per_seed, depth, positions_per_seed,
+        )
+        all_results.extend(results)
+    return all_results
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate NNUE training data using Stockfish"
+    )
+    parser.add_argument(
+        "--num-positions", type=int, default=1_000_000,
+        help="Target number of positions to generate",
+    )
+    parser.add_argument(
+        "--depth", type=int, default=8,
+        help="Stockfish analysis depth for labeling",
+    )
+    parser.add_argument(
+        "--output", type=str, default="data/training_data.npz",
+        help="Output .npz file path",
+    )
+    parser.add_argument(
+        "--stockfish", type=str, default="stockfish",
+        help="Path to Stockfish binary",
+    )
+    parser.add_argument(
+        "--workers", type=int, default=max(1, os.cpu_count() // 2),
+        help="Number of worker processes",
+    )
+    args = parser.parse_args()
+
+    # Verify Stockfish is available
+    try:
+        engine = chess.engine.SimpleEngine.popen_uci(args.stockfish)
+        engine.quit()
+    except Exception as e:
+        print(f"Error: Cannot find Stockfish at '{args.stockfish}': {e}")
+        print("Install Stockfish or provide path with --stockfish")
+        sys.exit(1)
+
+    print(f"Generating {args.num_positions} positions at depth {args.depth}")
+    print(f"Using {args.workers} workers")
+    print(f"Output: {args.output}")
+
+    seed_fens = SEED_POSITIONS
+    positions_per_seed = (args.num_positions // len(seed_fens)) + 1
+    games_per_seed = max(10, positions_per_seed // 30)
+
+    # Split seeds across workers
+    chunks = [[] for _ in range(args.workers)]
+    for i, fen in enumerate(seed_fens):
+        chunks[i % args.workers].append(fen)
+
+    worker_args = [
+        (chunk, args.stockfish, games_per_seed, args.depth, positions_per_seed)
+        for chunk in chunks
+        if chunk
+    ]
+
+    start_time = time.time()
+
+    if args.workers == 1:
+        all_results = worker_fn(worker_args[0])
+    else:
+        with multiprocessing.Pool(args.workers) as pool:
+            results_list = pool.map(worker_fn, worker_args)
+        all_results = []
+        for r in results_list:
+            all_results.extend(r)
+
+    elapsed = time.time() - start_time
+    print(f"Generated {len(all_results)} positions in {elapsed:.1f}s")
+
+    if not all_results:
+        print("No positions generated!")
+        sys.exit(1)
+
+    # Deduplicate by keeping first occurrence
+    seen = set()
+    unique_results = []
+    for features, score, wdl in all_results:
+        key = tuple(features)
+        if key not in seen:
+            seen.add(key)
+            unique_results.append((features, score, wdl))
+
+    print(f"After dedup: {len(unique_results)} unique positions")
+
+    # Convert to numpy arrays
+    # Store as variable-length feature lists using a padded array
+    max_features = max(len(f) for f, _, _ in unique_results)
+    n = len(unique_results)
+
+    # Feature indices (padded with -1)
+    feature_indices = np.full((n, max_features), -1, dtype=np.int16)
+    feature_counts = np.zeros(n, dtype=np.int8)
+    scores = np.zeros(n, dtype=np.int16)
+    wdl_probs = np.zeros(n, dtype=np.float32)
+
+    for i, (features, score, wdl) in enumerate(unique_results):
+        feature_indices[i, :len(features)] = features
+        feature_counts[i] = len(features)
+        scores[i] = score
+        wdl_probs[i] = wdl
+
+    # Save
+    output_path = Path(args.output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    np.savez_compressed(
+        str(output_path),
+        feature_indices=feature_indices,
+        feature_counts=feature_counts,
+        scores=scores,
+        wdl_probs=wdl_probs,
+    )
+
+    file_size = output_path.stat().st_size / (1024 * 1024)
+    print(f"Saved to {args.output} ({file_size:.1f} MB)")
+    print(f"Positions: {n}, Score range: [{scores.min()}, {scores.max()}]")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/train_nnue.py b/scripts/train_nnue.py
new file mode 100644
index 0000000..ad5c61c
--- /dev/null
+++ b/scripts/train_nnue.py
@@ -0,0 +1,349 @@
+#!/usr/bin/env python3
+"""
+Train an NNUE evaluation network from Stockfish-labeled positions.
+
+Architecture (dual-perspective, following Stockfish pattern):
+    Input: 768 sparse binary features (per perspective)
+    Feature transformer: Linear(768, 128) with shared weights
+    Combine: concat(SCReLU(stm_acc), SCReLU(nstm_acc)) -> 256
+    Output: Linear(256, 1)
+    Parameters: ~100K
+
+Loss: Sigmoid-scaled MSE + WDL cross-entropy
+
+Usage:
+    uv run --extra nn python scripts/train_nnue.py \
+        --data data/training_data.npz \
+        --output moonfish/models/nnue_v1.npz \
+        --epochs 20
+"""
+
+import argparse
+import math
+import sys
+from pathlib import Path
+
+import numpy as np
+
+try:
+    import torch
+    import torch.nn as nn
+    import torch.optim as optim
+    from torch.utils.data import DataLoader, Dataset, random_split
+except ImportError:
+    print("PyTorch is required for training. Install with: uv pip install torch")
+    sys.exit(1)
+
+
+NUM_FEATURES = 768
+HIDDEN_SIZE = 128
+SCORE_SCALE = 400.0  # Stockfish standard sigmoid scaling
+
+
+class SCReLU(nn.Module):
+    """Squared Clipped ReLU: screlu(x) = clamp(x, 0, 1)^2"""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.clamp(x, 0.0, 1.0) ** 2
+
+
+class NNUEModel(nn.Module):
+    """
+    Dual-perspective NNUE model.
+
+    The feature transformer is shared between both perspectives.
+    During training, we construct both perspectives from the input features.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.ft = nn.Linear(NUM_FEATURES, HIDDEN_SIZE)
+        self.screlu = SCReLU()
+        self.output = nn.Linear(HIDDEN_SIZE * 2, 1)
+
+    def forward(
+        self,
+        white_features: torch.Tensor,
+        black_features: torch.Tensor,
+        stm_is_white: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Forward pass.
+
+        Args:
+            white_features: (batch, 768) binary features from white's perspective
+            black_features: (batch, 768) binary features from black's perspective
+            stm_is_white: (batch,) bool tensor, True if side to move is white
+        """
+        white_acc = self.ft(white_features)
+        black_acc = self.ft(black_features)
+
+        white_out = self.screlu(white_acc)
+        black_out = self.screlu(black_acc)
+
+        # Assemble dual perspective: [stm, nstm]
+        stm_mask = stm_is_white.unsqueeze(1).float()
+        stm = white_out * stm_mask + black_out * (1 - stm_mask)
+        nstm = black_out * stm_mask + white_out * (1 - stm_mask)
+
+        hidden = torch.cat([stm, nstm], dim=1)
+        return self.output(hidden).squeeze(1)
+
+
+def feature_index(piece_type: int, color: int, square: int) -> int:
+    """White-perspective feature index."""
+    return (piece_type - 1) * 128 + color * 64 + square
+
+
+def feature_index_flipped(piece_type: int, color: int, square: int) -> int:
+    """Black-perspective feature index (flip colors and squares)."""
+    flipped_color = 1 - color
+    flipped_square = square ^ 56
+    return (piece_type - 1) * 128 + flipped_color * 64 + flipped_square
+
+
+class NNUEDataset(Dataset):
+    """Dataset for NNUE training from generated .npz data."""
+
+    def __init__(self, data_path: str):
+        data = np.load(data_path)
+        self.feature_indices = data["feature_indices"]  # (N, max_features)
+        self.feature_counts = data["feature_counts"]  # (N,)
+        self.scores = data["scores"].astype(np.float32)  # (N,)
+        self.wdl_probs = data["wdl_probs"].astype(np.float32)  # (N,)
+
+    def __len__(self) -> int:
+        return len(self.scores)
+
+    def __getitem__(self, idx: int):
+        # Build white-perspective and black-perspective feature vectors
+        white_features = np.zeros(NUM_FEATURES, dtype=np.float32)
+        black_features = np.zeros(NUM_FEATURES, dtype=np.float32)
+
+        count = self.feature_counts[idx]
+        indices = self.feature_indices[idx, :count]
+
+        for w_idx in indices:
+            if w_idx < 0:
+                continue
+            white_features[w_idx] = 1.0
+
+            # Decode the feature index to get piece info
+            pt_idx = w_idx // 128  # 0-5 (piece type - 1)
+            remainder = w_idx % 128
+            color = remainder // 64  # 0=white, 1=black
+            square = remainder % 64
+
+            # Compute black-perspective index
+            b_idx = feature_index_flipped(pt_idx + 1, color, square)
+            black_features[b_idx] = 1.0
+
+        score = self.scores[idx]
+        wdl = self.wdl_probs[idx]
+
+        # Determine side to move from score perspective
+        # The scores in training data are from white's perspective
+        # For now, we treat all as white-to-move and the model learns
+        # the perspective via the dual accumulator
+        stm_is_white = True
+
+        return (
+            torch.from_numpy(white_features),
+            torch.from_numpy(black_features),
+            torch.tensor(stm_is_white, dtype=torch.bool),
+            torch.tensor(score, dtype=torch.float32),
+            torch.tensor(wdl, dtype=torch.float32),
+        )
+
+
+def sigmoid_scale(x: torch.Tensor) -> torch.Tensor:
+    """Apply sigmoid scaling with Stockfish standard factor."""
+    return torch.sigmoid(x / SCORE_SCALE)
+
+
+def compute_loss(
+    predicted: torch.Tensor,
+    target_score: torch.Tensor,
+    target_wdl: torch.Tensor,
+    wdl_lambda: float = 0.5,
+) -> torch.Tensor:
+    """
+    Sigmoid-scaled MSE + WDL cross-entropy loss.
+
+    pred_sigmoid = sigmoid(predicted / 400)
+    target_sigmoid = sigmoid(target_score / 400)
+    loss = (1 - lambda) * MSE(pred_sigmoid, target_sigmoid)
+         + lambda * BCE(pred_sigmoid, target_wdl)
+    """
+    pred_sig = sigmoid_scale(predicted)
+    target_sig = sigmoid_scale(target_score)
+
+    # Sigmoid-scaled MSE
+    mse_loss = torch.mean((pred_sig - target_sig) ** 2)
+
+    # WDL binary cross-entropy (clamped to avoid log(0))
+    eps = 1e-7
+    pred_clamped = torch.clamp(pred_sig, eps, 1 - eps)
+    wdl_loss = -torch.mean(
+        target_wdl * torch.log(pred_clamped)
+        + (1 - target_wdl) * torch.log(1 - pred_clamped)
+    )
+
+    return (1 - wdl_lambda) * mse_loss + wdl_lambda * wdl_loss
+
+
+def train(args):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # Load dataset
+    print(f"Loading data from {args.data}...")
+    dataset = NNUEDataset(args.data)
+    print(f"Total positions: {len(dataset)}")
+
+    # Train/val split
+    val_size = int(len(dataset) * 0.1)
+    train_size = len(dataset) - val_size
+    train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+    print(f"Train: {train_size}, Val: {val_size}")
+
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=min(4, args.workers),
+        pin_memory=True,
+    )
+    val_loader = DataLoader(
+        val_dataset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=min(4, args.workers),
+        pin_memory=True,
+    )
+
+    # Model
+    model = NNUEModel().to(device)
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Model parameters: {total_params:,}")
+
+    optimizer = optim.Adam(model.parameters(), lr=args.lr)
+
+    # LR schedule: reduce by 3x every 3 epochs
+    scheduler = optim.lr_scheduler.StepLR(
+        optimizer, step_size=3, gamma=1.0 / 3.0
+    )
+
+    best_val_loss = float("inf")
+
+    for epoch in range(1, args.epochs + 1):
+        # Training
+        model.train()
+        train_loss = 0.0
+        n_batches = 0
+
+        for batch in train_loader:
+            white_feat, black_feat, stm, score, wdl = [
+                b.to(device) for b in batch
+            ]
+
+            optimizer.zero_grad()
+            predicted = model(white_feat, black_feat, stm)
+            loss = compute_loss(predicted, score, wdl, args.wdl_lambda)
+            loss.backward()
+            optimizer.step()
+
+            train_loss += loss.item()
+            n_batches += 1
+
+        train_loss /= max(n_batches, 1)
+
+        # Validation
+        model.eval()
+        val_loss = 0.0
+        n_val_batches = 0
+
+        with torch.no_grad():
+            for batch in val_loader:
+                white_feat, black_feat, stm, score, wdl = [
+                    b.to(device) for b in batch
+                ]
+                predicted = model(white_feat, black_feat, stm)
+                loss = compute_loss(predicted, score, wdl, args.wdl_lambda)
+                val_loss += loss.item()
+                n_val_batches += 1
+
+        val_loss /= max(n_val_batches, 1)
+
+        lr = optimizer.param_groups[0]["lr"]
+        print(
+            f"Epoch {epoch:3d}/{args.epochs} | "
+            f"train_loss={train_loss:.6f} | "
+            f"val_loss={val_loss:.6f} | "
+            f"lr={lr:.6f}"
+        )
+
+        scheduler.step()
+
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            # Save best model weights in numpy format
+            save_weights(model, args.output)
+            print(f"  -> Best model saved to {args.output}")
+
+    print(f"\nTraining complete. Best val_loss: {best_val_loss:.6f}")
+
+
+def save_weights(model: NNUEModel, output_path: str):
+    """Export model weights to numpy .npz format for inference."""
+    output = Path(output_path)
+    output.parent.mkdir(parents=True, exist_ok=True)
+
+    state = model.state_dict()
+
+    # Feature transformer: w1 = (768, 128), b1 = (128,)
+    w1 = state["ft.weight"].cpu().numpy().T  # PyTorch stores (out, in), we want (in, out)
+    b1 = state["ft.bias"].cpu().numpy()
+
+    # Output layer: w2 = (256, 1), b2 = (1,)
+    w2 = state["output.weight"].cpu().numpy().T  # (256, 1)
+    b2 = state["output.bias"].cpu().numpy()
+
+    np.savez(
+        str(output),
+        w1=w1.astype(np.float32),
+        b1=b1.astype(np.float32),
+        w2=w2.astype(np.float32),
+        b2=b2.astype(np.float32),
+    )
+
+    file_size = output.stat().st_size / 1024
+    print(f"  Weights saved: w1={w1.shape}, b1={b1.shape}, w2={w2.shape}, b2={b2.shape}")
+    print(f"  File size: {file_size:.1f} KB")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Train NNUE evaluation network")
+    parser.add_argument(
+        "--data", type=str, required=True,
+        help="Path to training data .npz file",
+    )
+    parser.add_argument(
+        "--output", type=str, default="moonfish/models/nnue_v1.npz",
+        help="Output path for trained weights",
+    )
+    parser.add_argument("--epochs", type=int, default=20)
+    parser.add_argument("--batch-size", type=int, default=16384)
+    parser.add_argument("--lr", type=float, default=0.001)
+    parser.add_argument(
+        "--wdl-lambda", type=float, default=0.5,
+        help="Weight for WDL loss (0=pure MSE, 1=pure WDL)",
+    )
+    parser.add_argument("--workers", type=int, default=4)
+
+    args = parser.parse_args()
+    train(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/uv.lock b/uv.lock
index 7accfb2..fa7885f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,6 @@
 version = 1
-revision = 2
-requires-python = ">=3.10"
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+requires-python = ">=3.12"
 resolution-markers = [
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+
+[[package]]
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+version = "4.12.1"
+source = { registry = "https://pypi.org/simple" }
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+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
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     { name = "flask-cors" },
     { name = "isort" },
     { name = "mypy" },
+    { name = "numpy" },
     { name = "parameterized" },
     { name = "pyinstaller" },
     { name = "types-flask-cors" },
     { name = "ufmt" },
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+[package.optional-dependencies]
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+]
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+    { name = "fastapi" },
+    { name = "httpx" },
+    { name = "pydantic" },
+    { name = "uvicorn", extra = ["standard"] },
+]
+
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     { name = "chess" },
     { name = "click" },
+    { name = "fastapi", marker = "extra == 'rl'", specifier = ">=0.100.0" },
     { name = "flake8" },
     { name = "flask" },
     { name = "flask-cors" },
+    { name = "httpx", marker = "extra == 'rl'", specifier = ">=0.24.0" },
     { name = "isort" },
     { name = "mypy" },
+    { name = "numpy" },
     { name = "parameterized" },
+    { name = "pydantic", marker = "extra == 'rl'", specifier = ">=2.0.0" },
     { name = "pyinstaller" },
+    { name = "torch", marker = "extra == 'nn'", specifier = ">=2.0.0" },
     { name = "types-flask-cors", specifier = ">=6.0.0.20250520" },
     { name = "ufmt" },
+    { name = "uvicorn", extras = ["standard"], marker = "extra == 'rl'", specifier = ">=0.23.0" },
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+provides-extras = ["nn", "rl"]
 
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