IBM vs DoorDash: Interview Question Comparison

When preparing for technical interviews, understanding a company's specific focus areas is crucial for efficient study. IBM and DoorDash represent two distinct ends of the software engineering interview spectrum—one a legacy tech giant with a broad focus, the other a modern delivery platform with deep operational complexity. A direct comparison of their question banks reveals significant differences in volume, difficulty, and core topics, which should directly inform your preparation strategy.

Question Volume and Difficulty

The most immediate difference is sheer volume. IBM's list of 170 questions is more than double DoorDash's 87. This suggests IBM's question bank covers a wider surface area of computer science fundamentals, likely reflecting the diverse range of roles and projects across the company.

The difficulty distribution is more revealing. IBM's questions are heavily weighted toward Easy (52) and Medium (102), with only 16 Hard questions. This points to an interview process that tests strong foundational competency and problem-solving approach, rather than extreme algorithmic optimization. In contrast, DoorDash's distribution is skewed toward Medium (51) and Hard (30), with a mere 6 Easy questions. This signals a process designed to identify candidates who can handle complex, scalable systems and intricate logic under pressure—key for a real-time logistics platform.

Topic Overlap

Both companies emphasize Array and String problems, which form the bedrock of most coding interviews. Mastery here is non-negotiable for either.

The divergence in secondary topics is telling. IBM's list highlights Two Pointers and Sorting. These are classic techniques for solving array and string problems efficiently and are common in interviews assessing clean, logical code.

# IBM-style Two Pointers example: Pair with Target Sum
def pair_with_target_sum(arr, target):
    left, right = 0, len(arr) - 1
    while left < right:
        current_sum = arr[left] + arr[right]
        if current_sum == target:
            return [left, right]
        if current_sum < target:
            left += 1
        else:
            right -= 1
    return [-1, -1]

DoorDash's key secondary topics are Hash Table and Depth-First Search (DFS). Heavy use of hash tables points to problems involving frequency counting, memoization, and designing efficient lookups—essential for features like matching orders, drivers, and restaurants. The prominence of DFS indicates a strong focus on graph and tree traversal problems, which model real-world hierarchies (menu structures, location grids) and state exploration.

# DoorDash-style DFS example: Clone a graph (simplified adjacency list)
def clone_graph(node, visited={}):
    if not node:
        return None
    if node in visited:
        return visited[node]

    clone = Node(node.val)
    visited[node] = clone

    for neighbor in node.neighbors:
        clone.neighbors.append(clone_graph(neighbor, visited))
    return clone

Which to Prepare for First

Prepare for IBM first if you are earlier in your interview preparation journey. The larger volume of Easy and Medium problems on fundamental topics provides a broader, more forgiving foundation. Solving these will solidify your core skills in array/string manipulation and basic algorithms, which are transferable to any interview, including DoorDash's.

Shift focus to DoorDash once your fundamentals are strong and you need to level up on complexity. The high concentration of Medium and Hard problems, especially those involving hash tables and DFS/graphs, requires deeper practice. This preparation is highly specialized and will push your problem-solving to the level needed for complex system design and optimization challenges.

In short, IBM's list is a better general study guide, while DoorDash's is a targeted workout for high-difficulty, systems-oriented problems.

For detailed question lists, visit the IBM and DoorDash company pages: IBM Interview Questions | DoorDash Interview Questions