disney

Disney Music Group (DMG) AI Engineer Case Interview — Rights‑Aware Personalization, Audio ML, and Brand Safety

This case mirrors Disney’s guest-first, brand-safe, and storytelling-driven interview style and focuses on how an AI Engineer would design and deliver ML systems for Disney Music Group’s catalog and franchises. The session emphasizes technical depth, product judgment, and alignment with Disney values (Safety, Courtesy/Collaboration, Show/Quality, Efficiency) while partnering with Music Publishing, Legal, Content Security, and Editorial. Case scenario: You are the AI engineer tasked with designing a rights-aware, family-safe soundtrack personalization and audio-intelligence platform for DMG. Your solution should power discovery (e.g., Disney Hits-style playlists), internal creative workflows (music supervision for trailers/series), and concerts/programming, while honoring territorial rights, audience age settings, and brand guidelines. Structure and timing: - 0–10 min: Discovery and clarifying questions • Identify primary goals (guest experience, brand trust, catalog coverage) and constraints (rights/territories, kids/teen profiles, localization, latency, scale). • Confirm cross-functional inputs (Music Supervisors, Legal, Data Privacy, Label Ops) and success criteria. - 10–35 min: System design — end-to-end architecture • Data: ingest audio masters, stems, lyrics, editorial notes, franchise metadata; unify IDs; track rights windows and territories. • Modeling: multi‑modal embeddings (audio spectrograms, lyrics text, metadata), mood/energy/tempo tagging, explicit/brand-safety classifiers, language detection, and similarity search. • Personalization: session-based and long-term recommenders; cold‑start for new releases; editorial override and pinning for marquee titles (e.g., franchise launches). • Policy & compliance layer: COPPA-aware handling for under‑13 experiences, GDPR/CCPA data minimization, territory gating, age and content filters, audit logs. • Serving: low-latency retrieval (ANN), feature store, A/B experimentation, rollbacks, observability; APIs for playlists and creative search (e.g., “uplifting, orchestral, no vocals, PG”). • Reliability: SLAs/SLOs, canary deploys, disaster recovery, abuse prevention. - 35–50 min: Modeling deep dive • Choose architectures (e.g., CNN/Transformer audio encoders, multilingual text encoders) and training data strategy (self‑supervision + limited labeled sets, active learning with editor feedback). • Guardrails: explicit/brand-safety detection; handling covers, medleys, and regional versions; bias and fairness across languages/genres. • Evaluation: offline metrics (AUC for safety, recall@K for search), robustness, and calibration; online metrics and gated launches for kids profiles. - 50–60 min: Experiment plan & measurement • Primary: completion rate, playlist saves, discovery of catalog breadth, retention. • Guardrails: zero rights violations, zero unsafe-content incidents, latency budgets, editorial satisfaction; region/language parity. • Rollout: staged markets, franchise tie-ins, holdbacks for measurement. - 60–70 min: Risks, tradeoffs, and ops • Rights changes and takedowns; franchise release days; novelty vs. familiarity balance; cost controls (inference/embedding refresh cadence); human-in-the-loop review. - 70–75 min: Storytelling wrap • 60-second exec narrative: how the system delights guests, protects the brand, and supports DMG partners. Deliverables expected in-session: - A whiteboarded architecture with data flows, policy/rights gates, and serving stack. - API sketch (e.g., POST /v1/search-tracks, GET /v1/playlists?profile=kids_US) with filters for territory, rating, language, vocals, mood. - Model approach and training plan with labeling strategy and editor-in-the-loop. - Experiment design (KPIs, guardrails, segmentation, ramp plan) and rollout playbook. Evaluation rubric (Disney-weighted): - Technical & ML design (40%): sound architecture, feasible modeling choices, latency/cost thinking. - Product & guest experience (25%): clarity of goals, launch strategy, editorial integration, storytelling. - Brand safety, rights, and privacy (20%): concrete controls, audits, compliance-by-design. - Communication & collaboration (15%): clarity, structured tradeoffs, partnership with Legal/Editorial. Interviewer prompts (used if needed): - How would you launch a kids-safe experience on day one with limited labels? What’s your fallback when the classifier is uncertain? - A major franchise soundtrack drops globally at midnight: describe your freshness strategy without sacrificing safety/rights. - A supervisor needs “tension, 90–110 BPM, no lyrics, culturally neutral” across multiple locales—how does your search rank and filter? - Rights window changes post-recommendation: how do you retroactively correct experiences and logs? What ‘great’ looks like: - Policy and rights are first-class citizens in the design, not afterthoughts. - Practical ML choices with data lifecycle clarity and measurable guardrails. - A coherent narrative tying guest delight, DMG partnerships, and operational excellence.

engineering

8 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

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