Spotify AI Engineer Behavioral Interview — Values, Collaboration, and Experimentation

This behavioral interview at Spotify evaluates how an AI Engineer works within Spotify’s squad/tribe model, makes user-centric decisions, and upholds the company’s values while shipping ML/AI features at scale. Expect a conversational but structured session with scenario-based prompts tied to real product contexts (e.g., recommendations, search, personalization, safety). Interviewers probe for how you collaborate across Product, Design, Data Science, Research, and Platform teams, communicate trade-offs, and learn from experiments. Focus areas include: - Ways of Working: Operating in autonomous squads, aligning on goals/OKRs, and navigating ambiguity using the Think it → Build it → Ship it → Tweak it mindset; handling async, globally distributed collaboration. - Impact and Experimentation: Defining success metrics (e.g., engagement, satisfaction, long-term retention), designing/reading A/B tests, deciding when to ship vs. iterate, and running blameless post-mortems. - Responsible AI: Balancing personalization with privacy, safety, fairness, and artist/creator considerations; partnering with Legal/Policy on risk and transparency. - Product Sense for Audio: Translating vague discovery/personalization problems into deliverable ML work, scoping MVPs, and prioritizing under constraints; explaining complex models to non-ML stakeholders. - Growth Mindset and Feedback: Seeking/receiving feedback, mentoring within chapters/guilds, knowledge sharing, and fostering an inclusive, psychologically safe environment (DIBs mindset). Interviewers look for concrete, end-to-end examples that show ownership, bias for action balanced with scientific rigor, and clear communication tailored to audience. You should be ready to walk through a recent AI/ML project, your decision points, what went wrong, and how you iterated.