Atlassian AI Engineer Behavioural Interview — Values-Driven Collaboration and Responsible AI

This interview mirrors Atlassian’s structured, values-based behavioural format and is tailored for AI Engineers building features across Jira, Confluence, Bitbucket, and Trello. Expect a STAR-driven conversation with calibrated interviewers (often including a values-trained interviewer) focusing on how you make customer-centric, ethical, and collaborative decisions when shipping AI in an enterprise SaaS context. Format (approx. 60 minutes): - 5–10 min: Introductions, role/product context, your brief background. - 35–40 min: Deep-dive behavioural prompts with follow-ups to unpack decisions, trade-offs, and outcomes. - 10–15 min: Your questions about team, roadmap, and ways of working. What it assesses at Atlassian: - Alignment to Atlassian values: “Open company, no bullshit”; “Build with heart and balance”; “Don’t #@!% the customer”; “Play, as a team”; “Be the change you seek”. - Customer impact and safety: How you translate ambiguous AI opportunities into safe, shippable customer value; handling enterprise needs (privacy, data residency, security) and communicating risks to non-ML stakeholders. - Collaboration in a distributed org (Team Anywhere): Partnering asynchronously with PM, Design, Data/Research, Security, Legal, and Support using Confluence pages/decision logs, Jira tickets, Bitbucket reviews, and Trello boards; giving/receiving candid feedback. - Responsible AI judgment: Bias/fairness mitigation, evaluation design (offline/online), guardrail metrics, privacy-by-design, model/content safety, incident playbooks and postmortems. - Experimentation and delivery: Defining success metrics (e.g., precision/recall vs. task success, CSAT, latency, guardrails), A/B tests, incremental rollouts behind flags, monitoring drift and data quality, and documenting learnings. - Ownership and influence: Proactively identifying opportunities, writing clear RFCs/ADRs, driving alignment across time zones, and making pragmatic build/buy decisions. Signals interviewers look for: - Specific, metric-backed stories (before/after) that show customer benefit and risk management. - Clear explanation of trade-offs (quality vs. latency, safety vs. recall, cost vs. performance) and why you chose them. - Evidence of open communication, blameless postmortems, and cross-functional partnership. - Thoughtful approach to privacy, compliance, and enterprise trust. Common red flags: - Vague outcomes, heroics over teamwork, weak ethical/safety reasoning, or bypassing documentation/review. Come prepared with 3–5 STAR stories covering: launching an AI feature end-to-end; handling an incident or model failure; resolving a disagreement across functions; improving an evaluation or experimentation plan; and driving a change via an RFC/Confluence decision page.