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Atlassian interview notes, page 2 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·Technical Phone Screen

Apr 2026
Atlassian software engineer interview with a system design flavor, focused on building a rating system from scratch. The problem itself wasn't too bad but the follow-up on scaling and data structures is where things got interesting.
  • Design a simple movie rating system that supports adding ratings for items and querying the total score, average score, and number of ratings per item.
  • How would your design change if the system needed to handle a very large number of items or high-frequency streaming rating updates?

“I went with a hashmap keyed by item ID, storing count and cumulative score so average is just a division away.”

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Atlassian·Machine Learning Engineer·Onsite - Multi Round

Apr 2026
Three-part ML interview at Atlassian covering past project background, domain knowledge in the candidate's stated areas, and a system design problem around building a cross-product RAG system. The design portion was the real meat of it and went pretty deep.
  • Walk us through your past ML projects and what your contributions were.
  • Deep-dive questions on the ML areas you listed as expertise on your resume.
  • Design a cross-product RAG system that spans two products, for example Jira and Confluence. How would you handle ingestion, change-data-capture, chunking, embeddings, retrieval, permissions, and online evaluation?
  • How would you approach online evaluation for a RAG system like this in production?

“Pretty standard opener but it set the tone for everything after.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Atlassian system design round for a software engineer role, focused entirely on building an image crawler from scratch. The scope kept expanding as the conversation went on, which I wasn't fully ready for.
  • Design an image crawler that starts from one or more root URLs, crawls pages to arbitrary depth, and downloads and stores the images it finds.
  • How would you model the database schema to track URLs, their fetch state, crawl depth, parent links, and image metadata?
  • How would you handle failures in the crawl pipeline, including retries, a dead-letter queue, and resuming after a partial failure?
  • How would you implement rate limiting per domain and respect robots.txt rules across a distributed set of crawler workers?

“I started with the obvious stuff, a queue of URLs, workers pulling from it, storing images somewhere.”

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Atlassian·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Atlassian coding round, pretty much a pure algorithms session. The whole thing centered on interval problems and I spent most of my prep time on trees so that was fun.
  • Given a list of intervals each with a start and end time, solve a problem involving overlapping intervals. This could mean merging overlapping intervals, counting the maximum number that overlap at any single point, or finding the minimum number of meeting rooms needed to schedule them all without conflict.

“I knew the sort-by-start trick going in but fumbled when they asked me to extend it to the meeting rooms variant.”

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Atlassian·Software Engineer·Technical Phone Screen

Apr 2026
Atlassian technical phone screen that built on a previous org hierarchy problem, this time extending it to a DAG structure. Solid problem if you've done LCA before, but the multi-parent twist adds enough complexity to trip you up if you're not careful.
  • Given an org structure that forms a DAG (each org can have multiple parent orgs), and two employees who each belong to one or more orgs, find the deepest common organization that contains both employees.

“This was a follow-up to a tree-based LCA problem, so I had the basic idea ready.”

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Atlassian·Data Scientist·Technical Phone Screen

Apr 2026
Atlassian Data Scientist interview with a pretty involved coding problem around rate limiting. The question was more algorithmic than I expected for a DS role, and the constraints they added mid-explanation kept piling up.
  • Implement a function that takes a list of URLs (one per second, starting at t=0) and returns '200' or '429' for each request, enforcing two sliding-window rate limits per URL: at most 2 successful requests in any 5-second window and at most 5 in any 30-second window. Rejected requests must not count toward future windows. Aim for O(1) amortized time per request and O(U) space where U is distinct URLs seen in the last 30 seconds.

“My first instinct was to reach for a counter dict and i got partway through before realizing rejected requests not counting completely breaks a naive counter approach.”

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Atlassian·Product Manager·Hiring Manager Screen

Apr 2026
Interviewed for a PM role at Atlassian, just one question to go off of here so not much to say about the full loop.
  • Why do you want to work at Atlassian?

“Pretty standard motivation question.”

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Atlassian·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Atlassian for an ML Engineer role, basically one big question about a real mistake and what came after it.
  • Describe a significant mistake you made at work. How did you find out about it, how did you handle communicating it, and what did you actually change afterward?

“The part that tripped me up was the 'what did you change' bit.”

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Atlassian·Data Scientist·Onsite - Product Sense / Strategy

Apr 2026
Atlassian data scientist onsite covering fit and product strategy. Three questions across personal motivation, product sense, and a market expansion case. Nothing too surprising but the last question had some real depth to it.
  • Walk me through your background and what draws you to this role specifically.
  • What's a digital product you love, and what data would you use to make it better?
  • Should the company expand into a new international market next year? Walk through what data you'd gather and how you'd approach the go-to-market.

“Went fine.”

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Atlassian·Software Engineer·Technical Phone Screen

Apr 2026
Atlassian SWE interview that came down to a tree problem, specifically finding the lowest common ancestor between two leaf nodes in a company org chart modeled as a tree. Pretty focused session, one meaty algorithmic question with a complexity discussion tacked on.
  • You're given a tree where internal nodes are departments and leaves are employees. Given two employee leaf nodes, find the deepest department that contains both of them. Also walk through the time and space complexity of your solution.

“Classic LCA but dressed up in org chart clothing, which threw me for a second because I was thinking about it in terms of HR logic before I realized it's just lowest common ancestor on a tree.”

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