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Atlassian interview notes, page 4 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

May 2026
Atlassian coding round focused entirely on building a route registry from scratch, with a follow-up that added wildcard matching. Pretty solid problem, two distinct parts, and the design decisions in part two are where things got interesting.
  • Implement a route registry that maps URL-style paths to handler functions, supporting exact path matching with add() and lookup() methods.
  • Extend the route registry to support wildcard segments in paths, where a single asterisk matches any one path segment, and define how precedence between exact and wildcard matches should work.

“Part one felt straightforward, just a hash map from path string to handler.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Atlassian where the whole thing was basically one big rate limiter question. More depth required than I expected, especially once they pushed into distributed territory.
  • Design a rate limiter. Define the API (something like allow(client_id) returning a boolean), choose an algorithm, and walk through the trade-offs between approaches like fixed window, sliding window, token bucket, and leaky bucket. Also cover thread safety and how you'd extend this to a distributed system.

“I went with token bucket pretty quickly because I'd seen it before, but the interviewer kept pulling on the edges.”

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

May 2026
Atlassian ML Engineer system design round focused on building an enterprise RAG chatbot. The question was dense and covered basically every layer of the stack, from ingestion to eval. Left feeling like I did okay on the retrieval side but probably undersold the ACL enforcement piece.
  • Design an internal company chatbot that answers employee questions by pulling from heterogeneous internal data sources like wikis, docs, tickets, code repos, and HR systems. The system should support multi-turn conversations, cite sources, enforce per-document access controls, and meet latency and freshness requirements at scale.

“This was a single big question that kept expanding.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
Atlassian system design round for a software engineer role. The whole interview was basically one big question about a password validation service, but it branched into like six different directions fast.
  • Design a password validation service that enforces length, character requirements, dictionary word membership, and passport number exclusion. Walk through the full system including API design, data structures for lookups, and how you'd handle high request volume.
  • What are the security implications of telling a user exactly which validation rule their password failed?
  • How would you handle internationalization for this validation service, specifically for users in non-English speaking locales?
  • There's an inherent contradiction between requiring passwords to be valid dictionary words and standard security advice around password entropy. How do you think about that tension?
  • How would you design the dictionary data store to support high QPS validation while also allowing periodic updates to the wordlist?

“I started with the API shape which felt safe, just a POST endpoint returning valid/invalid plus maybe a reason code.”

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Atlassian·Data Scientist·Take-home Assignment

May 2026
Take-home assignment for a Data Scientist role at Atlassian. Pretty involved for a take-home, basically a full ML pipeline from data loading to model comparison to final predictions, all in one shot.
  • Given two CSVs (training and test sets with a binary outcome and six continuous features), write a complete ML pipeline: load the data, run diagnostics including missingness and collinearity checks, fit baseline logistic regression plus L1 and L2 regularized variants with cross-validated penalty selection, report ROC AUC and a threshold-dependent metric on the held-out set, compare models and justify your regularization choice, identify the most important features, and output final predicted probabilities with a confusion matrix.

“This took way longer than I expected.”

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

May 2026
System design round at Atlassian for an MLE role, focused entirely on building a chatbot service from scratch. Pretty broad scope and they wanted depth on basically every layer, which was a lot to cover in one session.
  • Design a chatbot service end-to-end, covering functional requirements like multi-turn conversation, context handling, tool/API calls, and fallback behavior.
  • What are the non-functional requirements for a chatbot system, and how do you think about latency, throughput, availability, safety filtering, and cost per request?
  • Walk through the high-level architecture of a chatbot service, including the API gateway, conversation manager, LLM inference layer, retrieval/knowledge base, vector store, caching, and logging.
  • How would you handle state and memory in a chatbot, specifically the difference between per-session and per-user memory, and short-term versus long-term storage?
  • How do you approach scaling and monitoring for a chatbot system, and what are the key trade-offs between model size and latency, RAG versus fine-tuning, and streaming versus batched generation?

“I started with multi-turn context and spent probably too long on it.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
Atlassian system design round for a software engineer role, focused entirely on building an image crawler from scratch. Pretty open-ended, which sounds fun until you realize they want you to cover everything from queue design to failure handling to DB schema in one session.
  • Design a web crawler that starts from a set of root URLs, discovers links, and downloads images, supporting unlimited scale and crawl depth.
  • How would you handle deduplication to avoid re-crawling the same URLs?
  • Walk through your queue and scheduler design, including how you'd enforce per-host rate limiting (politeness).
  • What would your database schema look like for storing crawl state and downloaded image metadata?
  • How would you design the failure and retry model, and what would you monitor?

“This is the kind of question that feels straightforward for about 90 seconds.”

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

May 2026
Atlassian ML Engineer system design round, pretty intense. The whole session was one big design problem around automated ticket classification and routing, and they went deep on basically every layer of the stack.
  • Design a service that lets client companies submit bug tickets and automatically classifies and routes each ticket to the correct internal team. Walk through functional requirements, architecture, the ML model, and how you'd handle scaling and mis-routes.
  • How would you design the classification model itself? What features would you use, how would you train it, and would you score tickets online or offline?
  • What non-functional requirements matter most for this system, and how would you define and track SLAs?
  • How would you handle mis-routes and re-routing when a ticket ends up assigned to the wrong team?

“This is basically a full system design plus ML design question crammed into one.”

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Atlassian·Software Engineer·Technical Phone Screen

May 2026
Atlassian software engineer interview with a coding problem centered on a recommendation engine built from rating data. The question had a deceptively simple setup but the complexity discussion and edge cases made it a real workout.
  • You have a list of (user, movie, rating) tuples. Write a function recommend(user) that returns movies the user hasn't seen, where at least one other user who shares a commonly-rated movie with the target user has rated that candidate movie 4 or 5. Also discuss time/space complexity in terms of users U, movies M, and ratings R, and how you'd handle deduplication or ordering of results.

“I started by building lookup maps: one from user to their rated movies, another from movie to all users who rated it.”

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Atlassian·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Atlassian for a software engineer role. The problem was building a top-N posts feature for a workspace, which sounds straightforward until you actually try to scope it properly.
  • Design a system that returns the top-N posts in a workspace, ranked by engagement, supporting time windows like the last 24 hours, 7 days, and 30 days, with low read latency and high write throughput.

“The first thing that tripped me up was defining 'top'.”

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