Notion Software Engineer Onsite Coding Questions
3+ questions from real Notion Software Engineer Onsite Coding rounds, reported by candidates who interviewed there.
What does the Notion Onsite Coding round test?
The Notion 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
Notion Software Engineer Onsite Coding Questions
## Problem Implement a `TODOList` class that manages tasks with priorities and optional deadlines. The list should always return the highest-priority, earliest-deadline task next. ```python import heapq from dataclasses import dataclass, field from datetime import datetime from typing import Optional @dataclass(order=True) class Task: priority: int # lower = higher priority deadline: Optional[datetime] id: int = field(compare=False) title: str = field(compare=False) class TODOList: def add(self, title: str, priority: int, deadline: Optional[datetime] = None) -> int: ... def next(self) -> Optional[Task]: ... def complete(self, task_id: int) -> bool: ... def pending(self) -> list[Task]: ... ``` **Example:** ``` add("File taxes", priority=1, deadline=2024-04-15) add("Buy milk", priority=2) next() -> Task("File taxes", priority=1, ...) ``` ## Follow-ups 1. How do you handle lazy deletion for `complete()` efficiently with a heap? 2. What happens if two tasks have equal priority and no deadline? 3. How would you add recurring tasks that re-insert after completion? 4. How would you persist and restore the list across process restarts?
## Problem Design a lightweight in-app logging library for a mobile client. The library must support multiple log levels, write logs to local disk without blocking the UI thread, and expose an API to upload buffered logs to a remote endpoint on demand. ```kotlin // Android (Kotlin) class MobileLogger(val maxBufferSize: Int, val logDir: File) { fun log(level: Level, tag: String, message: String) fun flush(): List<LogEntry> fun upload(endpoint: String, onComplete: (Boolean) -> Unit) } enum class Level { VERBOSE, DEBUG, INFO, WARN, ERROR } ``` **Scenario:** A user reports a crash. Your library should let the support team retrieve the last 500 log lines from their session without requiring a new build. ## Follow-ups 1. How do you ensure logs are not lost if the app is force-killed mid-write? 2. What strategy prevents the log directory from growing unbounded on a low-storage device? 3. How would you redact PII (emails, tokens) before writing to disk? 4. Describe how you would unit-test the disk-write path without hitting real I/O.
## Problem Build a mobile TODO application with the following requirements: add, complete, and delete tasks; persist state locally so data survives app restarts; sync with a REST backend when connectivity is restored. ```swift // iOS (Swift) protocol TaskRepository { func fetchAll() -> [Task] func add(title: String) -> Task func complete(id: UUID) func delete(id: UUID) func syncWithRemote(baseURL: URL, completion: @escaping (Result<Void, Error>) -> Void) } struct Task { let id: UUID var title: String var isCompleted: Bool var updatedAt: Date } ``` **Edge case:** A task is deleted locally while offline, then the server sends it back during sync. Define the conflict resolution strategy. ## Follow-ups 1. How do you handle the case where two devices edit the same task offline simultaneously? 2. Should sync be optimistic or pessimistic? Justify your choice. 3. How would you implement a soft-delete so deleted tasks can be recovered within 30 days? 4. What schema migration strategy would you use if Task gains a new required field?
What to Expect in the Notion Onsite Coding Round
The Notion 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 Notion 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 Notion 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 Notion 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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