Breadth-First Search Questions at Anduril: What to Expect

Breadth-First Search (BFS) is a core algorithmic technique for traversing graphs and trees level by level. At Anduril, a defense technology company building autonomous systems and sensing platforms, BFS isn't just an abstract concept—it's a practical tool for solving real-world spatial and network problems. Their interview data shows nearly a quarter of their technical questions (10 out of 43) involve BFS, indicating its importance for modeling environments, finding shortest paths in grids (like for a drone or robot), or analyzing network connectivity. Mastering BFS demonstrates you can think systematically about state exploration, a critical skill for working on their distributed hardware and software systems.

What to Expect — Types of Problems

Anduril's BFS questions typically fall into two categories, both emphasizing practical application over theoretical trickery.

1. Grid and Matrix Traversal: This is the most common pattern. You'll be given a 2D grid representing a map, an image, or a physical space. Problems involve finding the shortest path from point A to B, counting distinct regions (like connected land masses or sensor blobs), or propagating an effect (like a signal or contamination) through adjacent cells. Obstacles, weighted cells, or multiple starting points are common twists that test your ability to adapt the standard BFS template.

2. Tree and Graph Level-Order Analysis: While less frequent than grid problems, you may encounter classic level-order tree traversal or problems involving relational networks. These could model hierarchical data flows in a system or the shortest number of connections between entities in a network graph. The key is recognizing when a problem asks for "the minimum number of steps" or "process all neighbors before moving deeper"—these are classic BFS signals.

How to Prepare — Study Tips with One Code Example

Your preparation should focus on internalizing the standard BFS template and then practicing its adaptations. Start by writing the template from memory for each language. The core pattern uses a queue to explore nodes in order and a visited set to prevent cycles and redundant work.

The most critical adaptation for grid problems is the "directions array." This cleanly handles moving up, down, left, and right. Here is the essential pattern:

from collections import deque

def bfs_grid(grid, start_row, start_col):
    if not grid:
        return -1

    rows, cols = len(grid), len(grid[0])
    directions = [(1,0), (-1,0), (0,1), (0,-1)] # down, up, right, left
    queue = deque([(start_row, start_col)])
    visited = set([(start_row, start_col)])
    steps = 0

    while queue:
        for _ in range(len(queue)):
            r, c = queue.popleft()
            # Process cell (r, c) here. e.g., if it's the target, return steps

            for dr, dc in directions:
                nr, nc = r + dr, c + dc
                if 0 <= nr < rows and 0 <= nc < cols:
                    if (nr, nc) not in visited and grid[nr][nc] != 'OBSTACLE':
                        visited.add((nr, nc))
                        queue.append((nr, nc))
        steps += 1 # Increment after processing all cells at the current distance
    return -1 # Target not found

Recommended Practice Order

1. Master the Template: Implement basic BFS on a binary tree and a simple 4-direction grid from scratch.
2. Solve Shortest Path Problems: Practice "Shortest Path in Binary Matrix" and "Rotting Oranges." These directly apply the pattern.
3. Handle Multiple Sources: Solve "01 Matrix" and "Walls and Gates," which require initializing the queue with multiple starting points.
4. Tackle Slight Variations: Try "Number of Islands" (BFS for counting) and "Open the Lock" (BFS on an implicit graph).
5. Simulate Anduril's Focus: Prioritize grid-based shortest path problems with obstacles, as they most closely model navigation and spatial analysis for autonomous systems.

Practice Breadth-First Search at Anduril