Depth-First Search Questions at Anduril: What to Expect

Depth-First Search (DFS) is a core algorithm for exploring graphs and trees, making it essential for software roles at Anduril. The company builds advanced defense technology, including autonomous systems and sensor networks. These systems often model operational environments, communication networks, or decision trees as graphs. DFS is the practical tool for tasks like pathfinding in a constrained space, analyzing network connectivity, searching configuration states, or traversing hierarchical command structures. At Anduril, you're not just solving abstract puzzles; you're demonstrating you can implement the fundamental traversal logic that underpins real-world systems. With DFS appearing in roughly 20% of their technical question pool, proficiency is non-negotiable.

What to Expect — Types of Problems

Anduril's DFS questions typically focus on applied graph and tree manipulation. You will rarely see a simple "traverse and print" exercise. Expect problems that layer DFS onto domain-specific scenarios.

• Grid-Based Search: Modeling a physical area (like a drone's operational grid or a sensor coverage map) as a 2D matrix. Problems involve counting distinct regions, finding paths under constraints, or simulating propagation (e.g., signal reach). These are classic "number of islands" variants.
• Tree Path Analysis: Working with hierarchical data, such as a file system, organizational structure, or decision tree. Questions often ask for specific path properties—finding a path that sums to a target, identifying the longest sequential sequence, or validating hierarchical rules.
• State-Space Exploration: Using DFS to generate possible states or configurations from a starting point. This could involve exploring moves in a simulated environment, generating combinations of system parameters, or backtracking through a set of operational commands.

The key is recognizing the underlying graph: nodes (grid cells, tree nodes, system states) and edges (adjacency, parent-child links, possible state transitions).

How to Prepare — Study Tips with Code Example

Master both the recursive and iterative (stack-based) implementations. For interviews, recursion is often cleaner for tree problems, while an iterative approach can be safer for very deep graphs. Always clarify the graph's nature (acyclic? connected?) to avoid infinite recursion. Practice sketching the recursion tree for small examples to build intuition.

The most critical pattern is the DFS template for a grid. This involves marking cells as visited to avoid cycles and recursing in four (or eight) directions.

def dfs_grid(grid, r, c):
    # Base cases: out of bounds or invalid cell
    if (r < 0 or c < 0 or r >= len(grid) or c >= len(grid[0]) or
            grid[r][c] == 0):
        return

    # Process the current cell (e.g., mark as visited)
    grid[r][c] = 0

    # Recurse in all four directions
    directions = [(1,0), (-1,0), (0,1), (0,-1)]
    for dr, dc in directions:
        dfs_grid(grid, r + dr, c + dc)

Recommended Practice Order

Build competency in this sequence:

1. Fundamentals: Implement DFS on an adjacency list and a binary tree. Solve "Number of Islands" (LeetCode 200) until the grid pattern is automatic.
2. Core Variations: Practice "Max Area of Island" (695), "Binary Tree Paths" (257), and "Path Sum" (112). These solidify path handling and state accumulation.
3. Anduril-Relevant Complexity: Tackle problems that combine DFS with other concepts, like backtracking ("Word Search" - 79) or adjacency in more complex graphs ("Clone Graph" - 133). Focus on clean, bug-free implementation under time pressure.

Practice Depth-First Search at Anduril