Greedy Questions at Meta: What to Expect

Greedy algorithms are a small but critical part of Meta’s interview problem set. While they represent only about 8% of the company’s tagged questions, they frequently appear in technical screens and on-site interviews for roles across software engineering, infrastructure, and machine learning. The reason is practical: many real-world optimization problems at Meta’s scale—from scheduling tasks on servers to minimizing resource allocation in data centers—have efficient, near-optimal greedy solutions. Mastering these patterns demonstrates you can think about local optimal choices that lead to a globally efficient system, a valuable skill for building scalable products.

What to Expect — types of problems

Meta’s greedy questions tend to fall into a few predictable categories. You will most often encounter interval scheduling and merging problems, such as determining the minimum number of meeting rooms required or merging overlapping intervals. Array-based greedy problems are also common, where you make a series of optimal choices while traversing an array, like maximizing profit from stock trades or partitioning labels. Finally, string manipulation problems, such as reorganizing strings or checking if one string can be transformed into another via swaps, appear regularly. The key is that the problem will usually ask for a maximum, minimum, or feasibility check, and a brute-force solution would be too slow. The greedy approach often involves sorting first, then making a series of irrevocable, locally optimal decisions.

How to Prepare — study tips with one code example

Start by solidifying the core concept: a greedy algorithm builds up a solution piece by piece, always choosing the next piece that offers the most immediate benefit. To identify a greedy problem, look for optimal substructure (an optimal solution contains optimal solutions to subproblems) and the greedy-choice property (a locally optimal choice leads to a globally optimal solution). Practice is about pattern recognition. For each problem type, learn the classic algorithm. For intervals, it’s often sorting by end time. For array jumps, it’s about tracking the farthest reachable index.

A fundamental pattern is the "Jump Game" problem, which tests if you can reach the last index of an array where each element represents your maximum jump length from that position. The greedy approach is to track the farthest point you can reach as you iterate.

def canJump(nums):
    farthest = 0
    for i, jump in enumerate(nums):
        if i > farthest:
            return False
        farthest = max(farthest, i + jump)
        if farthest >= len(nums) - 1:
            return True
    return False

Recommended Practice Order

Build your competency systematically. First, master the foundational leetcode easy problems like "Best Time to Buy and Sell Stock" and "Assign Cookies" to internalize the greedy decision-making process. Next, tackle the core medium-difficulty patterns: "Merge Intervals," "Jump Game" variations, and "Task Scheduler." Finally, challenge yourself with a few hard problems like "Candy" or "Minimum Initial Energy to Finish Tasks" to see how greedy logic combines with other techniques. Always verbalize your reasoning during practice. In an interview, you must justify why your greedy choice is safe and optimal—this is as important as writing the code.

Practice Greedy at Meta