Atlassian Machine Learning Engineer interview questions

28 Machine Learning Engineers have shared notes from their Atlassian interviews, with the questions they were asked in each round. That is 27% of the 104 Atlassian interview notes on Screna AI.

28 Machine Learning Engineer interview notes

Atlassian·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for an ML Engineer role at Atlassian, focused entirely on values alignment and leadership signals. Standard stuff but the follow-up probing caught me off guard a few times.
  • Tell me about a time you pushed back on a decision or requirement you disagreed with.
  • Describe a situation where you drove a meaningful change or improvement in your team or process.
  • Can you give an example of helping someone else grow, through coaching or mentoring?
  • Walk me through a time you had to make progress on something without clear direction or requirements.
  • Tell me about a mistake you made and how you handled it.

“I had a decent story ready but the follow-up on how I handled the conflict after pushing back is where I fumbled.”

View Post

Atlassian·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Atlassian for an ML Engineer role, basically one big question about building an internal enterprise chatbot that integrates with Confluence, Jira, and similar tools. Heavy on architecture and production concerns, less about ML theory than I expected.
  • Design an internal enterprise chatbot that lets employees ask questions in natural language, pulling answers from internal tools like Confluence and Jira, while enforcing access control, supporting both document Q&A and action-based workflows, and keeping everything auditable. Walk through the high-level architecture, key components, and how you'd evaluate and monitor it in production.

“This is a beast of a question and I think I underestimated how many distinct sub-problems are packed into it.”

View Post

Atlassian·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Atlassian for an MLE role. The whole thing was one big design question about building a RAG system on top of Jira and Confluence, which sounds straightforward until you get into the permissions and freshness requirements.
  • Design a RAG system that lets employees ask questions across Jira and Confluence, including cross-product queries like 'what's the status of project X and where's the design doc for it?'
  • How would you handle document-level permissions so users only retrieve content they're authorized to see?
  • Jira updates very frequently while Confluence changes less often. How does your ingestion pipeline handle that difference?
  • How would you make sure the generated answers cite the source Jira issues or Confluence pages correctly?
  • How would you evaluate this system both offline before shipping and online once it's live?

“This was the whole interview basically.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Atlassian ML engineer interview, got a nested list problem that looked straightforward but had a twist on the weighting scheme. Took me a bit to realize the depth calculation was inverted compared to the standard version.
  • Given a nested list of integers, return the sum of all integers weighted by their depth, where deeper integers have LOWER weight (weight = max depth minus current depth). How would you approach this?

“The classic version weights deeper integers more heavily, so I started going down that path before re-reading the prompt.”

View Post

Atlassian·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
ML system design round at Atlassian for an MLE role. The whole session was one extended design question about building search for a product like Jira or Confluence, and they expected you to go pretty deep on almost every layer of the stack.
  • Design a search system for one of Atlassian's products, such as Jira issues or Confluence pages. Walk through the full stack from indexing to ranking to serving.
  • How would you build the indexing pipeline to handle continuous updates and deletes for documents at the scale of tens of millions?
  • What embedding model would you choose for semantic search here, and how would you get training data for fine-tuning it?
  • How would you evaluate the search system both offline and online?
  • How would you handle personalization in search results given that different users in the same project have different roles and activity patterns?

“This question sprawled in every direction.”

View Post

Atlassian·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
Atlassian ML Engineer interview focused almost entirely on designing a recommendation system for a product feed. It was a deep system design session and they really wanted to see you hold the whole pipeline in your head at once, from retrieval to serving to how you'd know if it's actually working.
  • Design a recommendation system for a product feed (like an e-commerce home feed). Walk through the full system including requirements, architecture, and how you'd evaluate it.
  • How would you handle cold start for new users and new items in this recommendation system?
  • What offline metrics would you use to evaluate the ranker, and how do they connect to what you actually care about in production?
  • How would you design the feature store and training pipeline to keep features fresh enough for a real-time feed?
  • How do you prevent feedback loops and ensure diversity in the recommendations over time?

“This was the whole interview basically.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Atlassian ML Engineer interview had a classic data structures problem that looks deceptively clean on the surface but punishes you if you haven't thought through the bucket approach before.
  • Design a data structure that supports increment, decrement, get-max-key, and get-min-key operations, all in O(1) average time.

“I knew a hashmap alone wouldn't cut it the moment they said O(1) for both max and min.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Atlassian ML Engineer interview with a coding round that included a classic string manipulation problem. Nothing too wild, but it's the kind of question that feels easy until you start second-guessing your cow-counting logic.
  • Given a secret number and a guess, return a hint string in the format 'xAyB' where x is the count of digits correct in both value and position (bulls), and y is the count of digits that appear in the secret but are in the wrong position (cows).

“The bulls part is easy, just a single pass comparison.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Coding round for an ML Engineer role at Atlassian. One algorithmic problem, interval scheduling flavor, the kind of thing that sounds straightforward until you're actually trying to explain your greedy logic out loud.
  • Given a list of bookings each with a start and end time, assign them to courts such that no two bookings on the same court overlap. Return the minimum number of courts required and a valid assignment of bookings to courts.

“Classic interval scheduling problem but the assignment part tripped me up a bit.”

View Post

Atlassian·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Atlassian ML engineer interview with a sliding window coding problem. Pretty standard stuff but the implementation details tripped me up more than I expected.
  • Design a MovingAverage class that takes a window size on initialization and supports a next() method which appends a new integer to the stream and returns the average of the most recent N values.

“Felt fine about the core idea (circular buffer or a deque), but I fumbled the edge case where the stream hasn't filled up to the window size yet.”

View Post