← Back to Directory

Grammarly interview notes, page 5 of 5

Mid-sized

Most of these Grammarly write-ups come from Software Engineers (39), then Product Managers (3). The most reported rounds are the technical phone screen (17), the system design round (16), the behavioral / leadership round (3) and the product sense round (2).

42 interview notes · updated Jul 2026

Grammarly·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Grammarly for a software engineer role. The whole thing was a deep dive into building a collaborative spreadsheet product from scratch, think Google Sheets but you have to justify every decision out loud.
  • Design an online spreadsheet service where multiple users can edit the same workbook simultaneously in real time.
  • How would you design the data model for workbooks, sheets, cells, and revision history?
  • Walk through your API design for opening a session, fetching a sheet, submitting edits, subscribing to live updates, and catching up after a reconnect.
  • How do you handle conflict resolution when two users edit the same cell or insert rows and columns at the same time?
  • How would you handle persistence, snapshots, and recovery if a server crashes mid-session?
  • How would you approach formula recalculation when a cell that other cells depend on gets updated?
  • How would you scale this system and keep latency low for users across different regions?

“This is a beast of a question.”

View Post

Grammarly·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Grammarly for a software engineer role. The whole thing was a single deep problem: build an AI chat layer on top of a spreadsheet product. Lots of ground to cover and the discussion kept branching into areas I hadn't fully thought through.
  • Design an AI chat interface for a spreadsheet product that translates natural-language commands into structured spreadsheet operations via a tool-calling layer.
  • How would you handle ambiguous user commands and design a confirmation flow before applying changes to the spreadsheet?
  • What are the main prompt injection and safety risks in this system, and how would you mitigate them?
  • How would you approach authorization and permission validation before executing spreadsheet operations triggered by the AI?
  • How would you think about latency, cost, and scaling for this AI-powered spreadsheet assistant?

“This is a big one.”

View Post