Dynamic Programming Questions at Grammarly: What to Expect

Dynamic Programming (DP) is a core algorithmic technique for optimizing solutions to complex problems by breaking them into overlapping subproblems. At Grammarly, which builds advanced AI writing assistants, DP is essential for solving problems in areas like text processing, sequence alignment (e.g., comparing strings for grammar suggestions), efficient resource allocation in cloud infrastructure, and optimizing NLP model inference. Mastering DP demonstrates your ability to handle computationally expensive tasks efficiently—a key skill for engineers working on Grammarly's large-scale, real-time systems.

What to Expect — Types of Problems

With DP comprising roughly 23% of Grammarly's technical interview questions, you can expect problems focused on practical applications. Common categories include:

• String/Sequence DP: Problems involving edit distance, longest common subsequence, or palindrome partitioning. These directly relate to text manipulation, a core part of Grammarly's domain.
• Knapsack/Subset Problems: Questions about partitioning, subset sums, or resource optimization, which may mirror backend challenges in feature management or deployment.
• 1D/2D Array DP: Classic problems like unique paths, minimum path sum, or house robber variants, testing your ability to model and optimize state transitions. The problems are typically of medium to hard difficulty, requiring you to not only implement a solution but also explain your reasoning and optimize for time and space complexity.

How to Prepare — Study Tips with One Code Example

Start by solidifying the core DP patterns: top-down (memoized recursion) and bottom-up (tabular). For any problem, practice these steps:

1. Define the state (dp[i] or dp[i][j] meaning).
2. Establish the recurrence relation (how states transition).
3. Determine base cases.
4. Decide the iteration order and compute the final answer.

A fundamental pattern is the Fibonacci sequence, which illustrates the transition from a naive recursive solution to an optimized DP one. The key is storing previously computed results to avoid exponential re-calculation.

def fib(n):
    if n <= 1:
        return n
    dp = [0] * (n + 1)
    dp[1] = 1
    for i in range(2, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

# Space-optimized version
def fib_optimized(n):
    if n <= 1:
        return n
    prev2, prev1 = 0, 1
    for _ in range(2, n + 1):
        curr = prev1 + prev2
        prev2, prev1 = prev1, curr
    return prev1

Recommended Practice Order

Build your skills progressively:

1. Foundations: Start with 1D problems (Fibonacci, climbing stairs, house robber).
2. String Sequences: Move to 2D problems for strings (longest common subsequence, edit distance).
3. Knapsack Variants: Practice 0/1 knapsack, subset sum, and partition problems.
4. Grammarly-Specific Focus: Prioritize string and sequence DP problems, as they have the closest domain relevance.
5. Mock Interviews: Simulate interview conditions by explaining your thought process aloud while solving.

Consistent, pattern-focused practice is more effective than memorizing solutions.

Practice Dynamic Programming at Grammarly