← Atlassian Interview Insights
Pretty standard opener but it set the tone for everything after.
Select 2-3 ML projects that showcase different skills (e.g., modeling, deployment, experimentation) and align with the role's focus on technical trade-offs and adaptability. For each, briefly describe the problem, your specific contributions, the technical decisions you made, and the measurable impact. Emphasize how you navigated ambiguity and balanced trade-offs.
Pro tip: Quantify your impact with metrics (e.g., 'improved model accuracy by 15%') and explicitly state what you would do differently now, showing growth and self-awareness. This demonstrates maturity and a learning mindset.
Briefly describe the project's goal, team size, and your role. Keep it concise to focus on your contributions.
Detail the specific tasks you owned, such as data preprocessing, model selection, feature engineering, or deployment. Use 'I' statements to clarify your impact.
Explain key decisions, alternatives considered, and why you chose a particular approach. Mention constraints like latency, cost, or data quality.
Describe how you handled unclear requirements, changing data, or shifting priorities. Emphasize iterative experimentation and learning.
Quantify the project's impact (e.g., accuracy, revenue, efficiency) and reflect on what you learned or would improve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part is humbling if you've been loose with your resume.
For each ML area listed on your resume, prepare a structured deep-dive that covers the problem, your approach, trade-offs, and impact. Use the STAR method to narrate specific projects, and be ready to discuss technical details, alternatives, and lessons learned. Tailor your answers to Atlassian's context by emphasizing collaboration, scalability, and data-driven decision-making.
Pro tip: Don't just list technologies; demonstrate deep understanding by discussing trade-offs and failures. Interviewers at Atlassian value humility and learning from mistakes as much as successes.
Briefly define the ML area and its relevance to the role, ensuring alignment with the interviewer's expectations. Ask if they want a specific project or a general overview.
Explain the business problem, dataset characteristics, and constraints. Highlight any data challenges and how you addressed them.
Walk through your methodology, including model selection, feature engineering, and evaluation metrics. Justify your choices and mention alternatives considered.
Articulate the trade-offs (e.g., accuracy vs. interpretability, latency vs. performance) and quantify the impact of your solution. Share metrics and business outcomes.
Summarize key learnings, what you would do differently, and how this experience applies to Atlassian's challenges. Invite follow-up questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the bulk of the interview and honestly the most interesting part.
Start by clarifying requirements and constraints, then walk through the end-to-end pipeline for each component (ingestion, CDC, chunking, embeddings, retrieval, permissions, evaluation), emphasizing how cross-product integration and permission enforcement are handled. Highlight trade-offs and justify design choices with respect to scalability, latency, and security.
Pro tip: Emphasize that permissions must be enforced at query time using the user's identity, not just at ingestion, to avoid stale access. Also, mention that chunking strategies should be product-aware (e.g., Jira issues vs. Confluence pages) to preserve context.
Ask about scale (number of documents, QPS), latency requirements, permission models, and cross-product use cases. This ensures the design meets actual needs.
Describe how to ingest data from both products using APIs/webhooks, and implement change-data-capture to keep the index up-to-date. Consider batch vs. streaming, and handling deletes.
Explain product-specific chunking (e.g., by issue or page section) and embedding generation. Discuss model choice, dimensionality, and storage in a vector database.
Detail hybrid retrieval (keyword + vector) and how to enforce permissions at query time by filtering results based on the user's access rights across both products.
Propose metrics (e.g., recall, latency, user feedback) and A/B testing to continuously evaluate and improve the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got folded into the design question but felt like its own thread.
Start by defining clear offline and online metrics that capture retrieval quality, generation quality, and end-user satisfaction. Then describe a layered evaluation strategy: offline benchmarks for regression testing, online A/B tests for causal impact, and continuous monitoring with guardrail metrics. Emphasize the need for human-in-the-loop evaluation and feedback loops to handle the open-ended nature of RAG outputs.
Pro tip: Frame online evaluation as a product experimentation problem, not just a model metric problem—tie every metric to a business KPI (e.g., task success, time-to-resolution) and highlight how you'd handle novelty effects and long-term holdbacks.
Identify primary metrics (e.g., answer relevance, factuality, user engagement) and guardrail metrics (e.g., latency, cost, toxicity). Map them to business outcomes like customer satisfaction or support ticket deflection.
Create a golden dataset with diverse queries and reference answers. Use automated metrics (e.g., RAGAS, faithfulness, context precision) and periodic human review to benchmark model changes before deployment.
Randomize users into control (current system) and treatment (new RAG variant). Measure primary and guardrail metrics with sufficient power, and consider interleaving or switchback designs for low-traffic scenarios.
Deploy real-time dashboards for key metrics and set alerts for anomalies. Collect implicit (clicks, dwell time) and explicit (thumbs up/down) user feedback to detect drift and surface failure cases.
Use techniques like CUPED to reduce variance, and maintain a long-term holdout group to measure sustained impact. Analyze segment-level effects to ensure fairness and identify improvement areas.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.