Grammarly Software Engineer Onsite Coding Questions
3+ questions from real Grammarly Software Engineer Onsite Coding rounds, reported by candidates who interviewed there.
What does the Grammarly Onsite Coding round test?
The Grammarly onsite coding round is the core technical evaluation. Software Engineer candidates typically see 2-3 algorithm and data structure problems. Problems range from medium to hard difficulty, and interviewers evaluate both correctness and code quality.
Top Topics in This Round
Grammarly Software Engineer Onsite Coding Questions
## Problem Merge or correct overlapping text edits or corrections in a document, handling conflicting spans of text changes. ## Likely LeetCode equivalent No direct unambiguous LC equivalent. ## Tags strings, sorting, intervals
## Problem Review the following Java code for a thread-safe counter. Identify all bugs, concurrency issues, and code quality problems. For each issue, explain the fix. ```java public class SharedCounter { private int count = 0; private List<Integer> history = new ArrayList<>(); public void increment() { count++; // (1) history.add(count); // (2) } public int getCount() { return count; // (3) } public List<Integer> getHistory() { return history; // (4) } public void reset() { count = 0; history.clear(); // (5) } } ``` **Issues to find:** - (1) Non-atomic read-modify-write on `count`. - (2) `ArrayList` is not thread-safe; concurrent adds cause data corruption. - (3) Stale read - no visibility guarantee without `volatile` or lock. - (4) Returning mutable reference leaks internal state. - (5) Non-atomic compound reset allows torn reads between `count=0` and `history.clear()`. ## Follow-ups 1. Rewrite using `AtomicInteger` and `CopyOnWriteArrayList`. What are the tradeoffs? 2. When would you use `synchronized` vs `ReentrantLock` vs `AtomicInteger`? 3. How would you write a unit test that reliably exposes the race condition in the original code? 4. Describe a scenario where `CopyOnWriteArrayList` performs poorly.
## Problem Implement a `Subject` class (observable) and a `Subscription` class (observer handle) following the observer pattern. Multiple observers can subscribe to a subject; each receives emitted values. Subscribers can unsubscribe at any time. ```python class Subscription: def unsubscribe(self): ... class Subject: def subscribe(self, on_next, on_error=None, on_complete=None) -> Subscription: ... def next(self, value): ... def error(self, err): ... def complete(self): ... ``` **Example:** ``` subj = Subject() values = [] sub = subj.subscribe(on_next=lambda v: values.append(v)) subj.next(1) subj.next(2) sub.unsubscribe() subj.next(3) # not received print(values) # [1, 2] ``` **Additional requirement:** After `complete()` or `error()` is called, subsequent `next()` calls are no-ops and new subscribers immediately receive the terminal event. ## Follow-ups 1. How would you add back-pressure support if the subscriber is slower than the producer? 2. Implement a `pipe(operator)` method that applies a transform (e.g., `map`, `filter`) to the subject's stream. 3. How does this pattern differ from Python's `asyncio` event streams? 4. How would you make `subscribe` and `next` thread-safe?
What to Expect in the Grammarly Onsite Coding Round
The Grammarly Software Engineer Onsite Coding round has a specific calibration purpose distinct from other rounds in the loop. Across 3+ verified reports on LeakCode for this exact round type, the consistent expectations: clear scoping of the problem before diving into a solution, explicit reasoning about complexity, structured handling of edge cases, and the ability to discuss trade-offs between two reasonable approaches.
Reports tagged with the Onsite Coding round at Grammarly show recurring patterns in difficulty and topic distribution. The Onsite Coding round is typically 45-60 minutes; the interviewer is calibrated against a specific rubric. The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the answer. It is the path: did you clarify, did you verbalize your approach, did you handle edge cases, and did you communicate throughout.
How To Prepare for This Specific Round
Filter the questions below to the most recent reports (past 6-12 months). Questions tagged for this exact round type from this exact company at this exact role level are the highest-signal data available. Older reports may reference questions that have since rotated out of the company's pool.
Practice 4-6 representative problems from this set under timed conditions. The goal is not memorization (companies rotate questions); the goal is to internalize the patterns the interviewer typically reaches for and the depth of follow-up to expect. Reports on LeakCode also tag the typical follow-up depth at this round type, which is the discriminating signal between hire and no-hire calibration.
Onsite Coding Round Timing and Format
The Onsite Coding round at Grammarly typically runs 45-60 minutes. Use the first 2-3 minutes to clarify requirements; you should never start coding or designing without verifying the input/output format, constraints, and edge cases out loud. Use the next 5-7 minutes to verbalize your approach before writing any code. The middle 20-30 minutes are implementation. Reserve the final 10 minutes for testing with concrete examples and discussing optimization or trade-offs.
Time budget discipline is one of the most reliable senior-vs-junior discriminators in this round. Strong candidates verbalize where they are in their budget out loud ("I've used about 20 minutes, I have 15 minutes left for testing and one optimization"). This signals engineering maturity to the interviewer and creates positive feedback they can capture in writing.
Common Failure Modes in This Round
Reports tagged "no hire" at Grammarly Software Engineer Onsite Coding commonly cite: coding silently without verbalizing approach, jumping to implementation before clarifying requirements, missing edge cases (empty input, single element, very large input), producing working code that the candidate cannot refactor when asked, and failing to test their solution with concrete examples before declaring done.
The single most predictive failure mode in 2025-2026 reports: not asking clarifying questions. Interviewers at all FAANG companies are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's notes.
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