Top 20 LeetCode Problems for Startup Interviews, Ranked by ROI

Most startup interview loops contain two coding problems. Not five. A Series B company hiring a backend engineer will give you 40 minutes per problem, one interviewer, and a CoderPad link. That's it. No system design deep dive from a VP who forgot what a linked list is. Just two problems and a verdict.

That changes your prep strategy completely. FAANG companies run enough volume to filter on rare algorithmic patterns and absorb the false negative rate. Startups can't. They reach for problems that reveal signal fast: can you write clean code, explain your thinking, and handle edge cases without being prompted?

This guide is for engineers prepping for startup interviews specifically. You'll get the 20 problems that actually show up, ordered from highest to lowest return on investment, with what each one teaches and why startups keep reaching for it.

What LeetCode Problems Do Startup Interviews Use?

Startup interview problems cluster around medium difficulty and practical clarity. The list below covers every pattern you'll face with realistic frequency: hash maps, sliding window, two pointers, linked lists, stack, trees, graphs, binary search, intervals, and one DP anchor. Niche hard problems (segment trees, suffix arrays, complex bitmask DP) are not on this list because they don't belong in startup interviews with any regularity.

Finish the first ten before the last ten. The ROI drops as you go down, not because the later problems are unimportant, but because the earlier ones appear in more interviews and transfer more broadly. For context on how startup loops differ from big tech, this breakdown covers what the difference actually means for your prep.

ROI tiers for the 20 startup interview problems organized by pattern family

Hash Maps

1. Two Sum (LC 1)

Every startup interview loop either asks this problem or a variant. If you've applied to more than three companies, you've already seen it. If not, check your spam folder.

The correct solution trades an array scan for a hash map: store complements, find them in O(1). If you can't derive this cleanly, explain the trade-off, and handle the duplicate-index edge case without hesitation, nothing else on this list matters yet. This is the entry point for the most common interview pattern: sacrificing space to buy time.

2. Longest Consecutive Sequence (LC 128)

The obvious solution sorts the array, costing O(n log n). The correct solution uses a HashSet and only starts counting from elements where n-1 is absent, visiting each sequence exactly once. The lesson is recognizing when sorting is a reflex you should question. Startups like this one because the gap between the intuitive and optimal approach is narrow enough to discuss in 15 minutes.

3. Group Anagrams (LC 49)

Sort each string and use the result as a dictionary key. All strings that share a key are anagrams. Sounds obvious once you see it. Takes a minute to see it the first time. The thinking pattern, "what makes two inputs equivalent?", applies across dozens of harder problems. Startups use it to test whether you can model grouping relationships without a nested loop.

Single-Pass Arrays

4. Best Time to Buy and Sell Stock (LC 121)

One loop, one variable tracking the minimum seen so far. Five to eight lines. The reason this earns its spot is communication, not algorithm. Startups put this problem early to see if you can reason out loud on something simple. A candidate who narrates before coding gives the interviewer something to work with for the rest of the session. Talk through it even when the answer feels obvious. Especially then.

5. Product of Array Except Self (LC 238)

No division allowed, O(n) time, O(1) extra space. Two passes: prefix products left-to-right, suffix products right-to-left, combined in place. The constraint is not decorative. This problem separates engineers who memorize solutions from ones who can construct them. Reading constraints carefully before jumping to code is the actual lesson.

Sliding Window

6. Longest Substring Without Repeating Characters (LC 3)

The gateway variable-width sliding window problem. A hash map or set tracks what's inside the window; the left pointer advances when a duplicate appears. Solve this one and you've understood the skeleton for 15 other problems. Startups use it as a filter for the sliding window pattern, which appears often in problems involving streams, buffers, and rate-limiting logic.

The full sliding window pattern is covered in depth here.

Two Pointers

7. Container with Most Water (LC 11)

Start with both ends. Move the shorter side inward. The proof: the taller side is never the bottleneck, so moving it can't help. It feels obvious after the fact. It doesn't during the interview. The real skill tested is whether you can reason about what you're eliminating at each step, not just what you're keeping. That elimination proof is the foundation of every converging two-pointer problem you'll see.

8. 3Sum (LC 15)

Sort, fix one element, run two pointers on the rest. The part most candidates get wrong is the deduplication logic, skipping elements equal to the previous when advancing the outer pointer. This is where technically correct solutions go to die. Startups ask this because it requires both pattern recognition and precise implementation. A clean, bug-free solution here signals a strong candidate more clearly than a brute-force attempt on something harder.

Linked Lists

9. Merge Two Sorted Lists (LC 21)

The dummy head node trick eliminates all special-casing for the first element. No clever algorithm. Ten to twelve lines of clean pointer manipulation. Startups use this problem specifically to evaluate code quality on something algorithmically trivial. If your solution is 25 lines with three conditional branches and one latent bug, that's what goes in the debrief.

10. Linked List Cycle (LC 141)

Floyd's cycle detection: fast pointer at two steps, slow at one, meeting guaranteed if a cycle exists. A HashSet solution also works, but costs O(n) space. The O(1) space version is what gets noticed. Startups ask this because it tests whether you know to check the space constraint before settling on the obvious approach.

Stack

11. Valid Parentheses (LC 20)

Push opening brackets. Match and pop on closing. Return false if mismatch or non-empty stack at the end. The algorithm is trivial; the evaluation is entirely about how you handle edge cases without being asked. Empty string? Single character? Mismatched types rather than counts? Every unprompted edge case you catch is a free point on the rubric.

12. Daily Temperatures (LC 739)

A decreasing monotonic stack. When you encounter a warmer day, pop everything it's warmer than and record the distance. This is the entry point to the monotonic stack pattern, which appears in histogram, interval, and span problems. Skip it and those variants will each feel like new problems.

Trees

13. Maximum Depth of Binary Tree (LC 104)

The recursive postorder solution: return 0 on None, then 1 + max(left, right). This problem forces you to think recursively in the correct direction. That postorder reasoning structure appears unchanged in every harder tree problem: diameter, path sum, balanced check. The problem looks like a warm-up. It is. Just not the kind you skip.

14. Binary Tree Level Order Traversal (LC 102)

BFS with a queue. The detail that matters: snapshot the queue length at the start of each level with for _ in range(len(queue)), then process exactly that many nodes. Without this snapshot, you'll mix elements from different levels. Startups ask this frequently and the output format, a list of lists, is where candidates often slip.

15. Validate Binary Search Tree (LC 98)

Don't compare a node only to its immediate parent. Propagate a valid range from root to leaves and check that every node falls inside it. The naive left-child/right-child comparison fails on nodes that violate the invariant imposed by a higher ancestor. This problem tests whether you understand what the BST property actually guarantees, not just what it looks like.

Graphs

16. Number of Islands (LC 200)

Grid DFS or BFS, marking visited cells. The outer loop over every cell is mandatory, not optional, because islands are disconnected by definition and a single traversal won't find all of them. The transferable skill is converting a grid into a graph in your head: each cell is a node, each adjacent valid cell is an edge. That reframe applies to every grid problem you'll face.

For the full BFS versus DFS decision, this comparison covers when each is the right choice.

17. Course Schedule (LC 207)

Model prerequisites as a directed graph, then detect cycles via Kahn's algorithm. Count processed nodes; if the count is less than total nodes, a cycle exists. Startups that build anything with dependencies, a build system, a task scheduler, a deployment pipeline, reach for this problem because it maps directly to real engineering. Understanding why the cycle detection works here matters more than memorizing the solution.

Binary Search

18. Search in Rotated Sorted Array (LC 33)

Binary search with one extra observation: in a rotated array, at least one half is always normally sorted. Determine which half, decide whether the target could be there, recurse. Candidates who try to find the rotation point first write twice as much code and introduce twice as many bugs. The clean solution requires trusting binary search's single-invariant structure.

Intervals

19. Merge Intervals (LC 56)

Sort by start time. Merge overlapping intervals by extending the current end. The non-obvious bug: using end instead of max(current_end, interval.end) silently truncates intervals that are fully contained inside the current one. It doesn't crash. It doesn't error. It just quietly returns wrong answers on a slice of inputs. Platform and infrastructure teams ask this because scheduling, resource allocation, and calendar logic produce exactly this structure in production.

Dynamic Programming

20. Coin Change (LC 322)

Initialize dp with infinity, set dp[0] = 0, then for each amount try every coin. Iterate forward because coins can be reused. DP is where a lot of candidates stall, spiral, and write three wrong base cases in a row. If you study one DP problem before a startup interview, make it this one. It forces you to understand state, transition, base case, and iteration direction all at once. The dynamic programming framework covers how to construct the recurrence from scratch if this pattern is new to you.

Practice Transfer, Not Recall

Don't grind all 20 in sequence. Group by pattern, solve two or three problems, then do an unseen problem in the same pattern without notes. The transfer is what you're training, not solution recall.

Time yourself. Startup interviews move faster than FAANG ones. If you can't reach a working solution in 25 minutes on any of the first ten problems, fix the foundation before moving further.

Most importantly: practice talking while you code. The actual interview condition involves an interviewer waiting for you to narrate your thinking. Ten sessions explaining your approach out loud is worth more than fifty silent LeetCode runs. You will feel ridiculous narrating to your laptop. Do it anyway.

SpaceComplexity runs voice-based mock interviews that score communication, problem-solving, and code quality on the same rubric real interviewers use. If you're preparing for startup interviews where each problem carries more weight, getting realistic feedback before the real thing matters more than adding a 21st problem to your list.