← Back to Directory

Atlassian interview notes, page 6 of 11

Large Enterprises

Most of these Atlassian write-ups come from Software Engineers (60), then Machine Learning Engineers (28) and Data Scientists (12). The most reported rounds are the technical phone screen (45), the system design round (38), the behavioral / leadership round (10) and the onsite coding round (3).

104 interview notes · updated Aug 2026

Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
Atlassian system design round focused entirely on tree data structures, which sounds narrow until you realize how many directions they can pull it. Heavy emphasis on database schema choices and REST API shape, less on raw coding.
  • Design a hierarchical tree data model that supports adding a node under a given parent and retrieving all descendants of a node. Walk through your REST API design and database schema choices.
  • What are the read and write trade-offs between adjacency list, materialized path, nested set, and closure table storage strategies for trees?
  • How would you handle edge cases like cycles, deletion of a subtree, moving a subtree to a different parent, and enforcing depth limits?

“I went straight to adjacency list because it felt obvious, and the interviewer just nodded and waited.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

May 2026
Atlassian ML Engineer interview that went deep on graph theory, specifically a nasty DAG variant of LCA that I was not expecting to come up in this context. The problem was genuinely hard and I fumbled parts of it, but it was an interesting conversation.
  • Given a Directed Acyclic Graph where nodes can have multiple parents, define what 'Lowest Common Ancestor' means in this setting and describe an algorithm to compute it.

“This wrecked me a little.”

View Post

Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
Atlassian system design round focused on a greeting card web app, MVP scope only. Pretty scoped down compared to what I expected but the follow-ups on scaling and auth made it more interesting than the prompt let on.
  • Design a web application for a basic greeting card service. Users should be able to write a new card and view the cards they've written. Cards only support text. Walk through the frontend, backend API, and datastore.
  • What does the data model look like for User and Card entities in this system?
  • Define the API endpoints needed to create a card, list all cards for a user, and view a single card.
  • How would you handle authentication for this service at the MVP stage?
  • What would need to change architecturally if this service had to scale well beyond the MVP?

“I started with the datastore because that felt most concrete, which in hindsight was a bit backwards.”

View Post

Atlassian·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral loop for a Data Scientist role at Atlassian. Five questions, all structured around specific situations with measurable outcomes. The whole thing felt like they wanted real stories, not polished talking points.
  • Why do you want to join Atlassian specifically? Connect your motivations to the products, the mission, and how the company operates, and name two real trade-offs you're accepting by joining.
  • Tell me about a time you gave someone constructive feedback. How did they react, what did you do after, and what actually changed?
  • How do you handle competing priorities? Walk me through a recent example, the explicit trade-offs you made, and what you took away from it.
  • Describe a time you went above and beyond to drive a customer-facing change, but it didn't work out. What did you try, how did you measure it, and what did you learn?
  • What's the most effective team you've ever been part of? What made it work, what rituals or behaviors kept it working, and what did you personally bring to it?

“The trade-offs part tripped me up a bit.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML Engineer role at Atlassian and got a coding question that looks deceptively simple on the surface. The key was knowing the right data structure going in, otherwise you end up with an O(n) solution and a quiet room.
  • Implement a MovingAverage class that computes the rolling average of the last k values from a continuous data stream, with O(1) time per insertion.

“I knew the circular buffer approach but blanked on articulating why it was O(1) for a second.”

View Post

Atlassian·Software Engineer·Technical Phone Screen

May 2026
Atlassian software engineering interview that went deep into tree data structures and algorithm design. The whole session revolved around one meaty problem with a lot of moving parts, and they really pushed on the tradeoffs between different preprocessing approaches.
  • Given a list of (employee, manager) pairs representing an organizational hierarchy, build a rooted tree structure from those pairs. What APIs would you define for construction and input validation, and how would you detect cycles or disconnected components?
  • For a set of k employees (k >= 2), implement a function that finds their nearest common ancestor in the organizational hierarchy. Walk through at least two different preprocessing strategies, analyze the time and space complexity of each, and implement one.
  • Write unit tests covering typical and edge cases for your LCA implementation, including employees in different subtrees, a missing employee, and the case where the root itself is the answer.

“This part felt more open-ended than I expected.”

View Post

Atlassian·Software Engineer·Technical Phone Screen

May 2026
Atlassian software engineer interview with a system design coding question focused on building an in-memory voting module. Pretty clean problem scope but the follow-up behavior questions tripped me up a bit.
  • Design an in-memory module where agents can rate items (like tickets or articles) with a 1-5 integer score. It should support computing average ratings per item and returning all items sorted by average rating descending, with ties broken by item ID ascending. Also handle edge cases: what if an agent votes on the same item twice, and what do you return for items with no votes?

“I jumped straight to hashmaps which was fine, but I fumbled the duplicate vote handling for longer than I should have.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

May 2026
Atlassian ML Engineer interview that was pretty much one meaty design question with a follow-up that kept going. The core problem was straightforward but the conversation spiraled into weighted average territory fast and I wasn't totally prepared for that part.
  • Design a data structure that, given a window size N and a stream of incoming integers, returns the rolling average of the last N values after each new value arrives. What's the time complexity per update?
  • How would you modify the design to give more weight to recent values? Walk through both an exponentially weighted moving average and a linearly decaying weight scheme, and compare the trade-offs between those approaches and the equal-weight rolling average.

“Got through this fine.”

View Post

Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Atlassian for a software engineer role. The question was a single sprawling beast about designing a Roblox-style avatar drawing and rendering service, covering basically everything from upload protocol to CDN to moderation. A lot to hold in your head at once.
  • Design the backend for a user-generated avatar drawing system (think Roblox-style), where users submit custom drawings and the service renders and serves the resulting images. Cover the upload protocol for drawing primitives, storage strategy for source vs rasterized formats, the render pipeline including sync vs async tradeoffs and GPU vs CPU workers, CDN distribution and cache invalidation on edits, public feed and sharing access, moderation hooks, and how to scale the renderer under load. Also discuss latency targets, cost, and consistency.

“This one sprawls in every direction and I think I let it.”

View Post

Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
Atlassian system design round for a software engineer role. The main problem was building a content popularity tracker with increment, decrement, and max-lookup operations. Pretty meaty discussion about data structures and distributed scaling.
  • Design a system that tracks content popularity on a social media platform, supporting increment, decrement, and a most-popular lookup. Walk through your choice of data structures, time complexity trade-offs, and how you'd scale this across multiple servers.

“My first instinct was a hash map plus a max-heap and I just went with it before thinking through the decrement case.”

View Post