Uber Leadership & Values Behavioral Interview for AI Engineer

Purpose: Assess alignment with Uber’s Leadership & Values (L&V) while validating how the candidate executes AI/ML work in high-scale, safety-critical, marketplace systems across Mobility, Delivery, and Freight. What this interview covers (tailored to Uber and AI Engineering): - Customer Obsession & Safety: How you translate rider/driver/merchant needs into ML/AI solutions; designing for safety, fraud, and trust; handling trade-offs that impact real people on the platform. - Ownership & Bias for Action: End-to-end responsibility for models/services (problem framing → design → launch → monitoring/on-call); stepping up during incidents; driving decisions amid ambiguity and incomplete data. - Data-Driven Decisions & Experimentation: Defining success metrics (e.g., ETA error, conversion, cancellation/defect rates, dispatch efficiency, cost-per-trip/delivery), running A/B tests and guardrails, interpreting results, and iterating responsibly. - One Uber Collaboration: Partnering with PMs, DS/ML researchers, platform/infra, mobile/backend, Ops, Legal/Privacy, and Safety; communicating clearly across time zones and orgs; valuing ideas over hierarchy. - Big, Bold Bets with Pragmatism: Balancing innovation (new models, platforms, LLM features) with reliability, latency, cost, and compliance at global scale; knowing when to simplify. - Integrity & Doing the Right Thing (Responsible AI): Privacy-by-design, fairness, explainability, policy alignment; handling sensitive data and preventing harm; raising concerns even when costly. - Perseverance & Learning: Postmortems, measurable learning from failures, and continuous improvement. Suggested 60-minute flow (behavioral-heavy): 1) 5 min — Warm-up & context: interviewer shares team charter; candidate gives 60–90s background focused on AI systems at scale. 2) 25–30 min — Two deep dives (STAR): - Story A (impact at scale): Marketplace/ML system with clear metrics and trade-offs. - Story B (ambiguity or incident): Incident/launch pivot, safety or integrity issue, or major stakeholder conflict. 3) 10 min — Uber-style situational scenario (choose one): - Scenario 1: ETA model drift is increasing cancellations in one mega-city; how do you triage, measure, and fix without harming other regions? - Scenario 2: New LLM feature improves support resolution but raises latency and cost; design guardrails and a ramp plan. 4) 5–7 min — Collaboration & values drill-down: stakeholder alignment, dissent, escalations, and decision logs. 5) 5 min — Candidate Q&A about team, roadmap, metrics, and expectations. Behavioral question bank (use 4–6, probe deeply): - Tell me about a time you owned an AI/ML project end-to-end under tight constraints. What trade-offs did you make and why? - Describe a high-ambiguity problem where requirements kept changing. How did you converge on a solution and measure success? - Share a time your model improved a core metric (e.g., dispatch/ETA/fraud). How did you validate impact and protect against regressions? - Give an example where you identified a safety, fairness, or privacy risk in an AI system. What action did you take? - Tell me about a serious production issue involving a model or data pipeline. How did you detect, mitigate, and prevent recurrence? - Describe a disagreement with a PM or research partner about methodology or launch criteria. How was it resolved? - When did you make a bold bet (new architecture/LLM/feature) and how did you de-risk it for global scale? - How have you balanced cost/latency with quality for online inference? What metrics or SLOs guided you? - Example of learning from an experiment that failed. What changed in your next iteration? - How do you ensure inclusive impact across regions/segments (e.g., new markets, device constraints, supply-demand dynamics)? AI-specific probe prompts (use as follow-ups): - Metrics: Which north-star and guardrail metrics? Any fairness/coverage metrics? Why those? - Experimentation: How did you design the ramp (city cohorts, time-of-day, driver segments)? What power and MDE assumptions? - Reliability: What on-call signals (drift, anomaly detection, feature freshness, data quality) and rollback playbook? - Safety/Compliance: How did privacy constraints shape data/feature choices? Any model cards or DPIAs? - Cost/Latency: Token/compute budgeting for LLMs; batching, distillation, or caching strategies. What good looks like at Uber (evaluation signals): - Clear STAR answers with quantified impact and explicit trade-offs; speaks to scale, real-time constraints, and global/regional nuances. - Demonstrates ownership beyond modeling (data contracts, platform integration, observability, on-call readiness). - Uses principled, data-driven decisions; can explain experiment design and failure modes. - Centers safety, integrity, and responsibility; surfaces risks proactively and proposes guardrails. - Collaborates across functions and cultures; documents decisions; values ideas over hierarchy. Red flags (behavioral): - Vague outcomes; no metrics or post-launch learning. - Over-index on model novelty vs. product impact, safety, or reliability. - Blames partners; poor stakeholder alignment; disregards local market differences. - Hand-wavy about experimentation or on-call readiness; lacks rollback/guardrail thinking. - Ignores privacy/fairness or downplays real-world harm. Scoring rubric (1–5): - 1: Superficial; no metrics; limited ownership; ignores risks. - 2: Some ownership; minimal quantification; weak collaboration or experiment rigor. - 3: Solid Uber fit; clear STAR stories with metrics; sound decisions; basic responsible-AI practices. - 4: Strong bar-raiser; leads ambiguous efforts; robust experimentation/observability; anticipates risks and scales solutions. - 5: Exemplary; repeatedly drives cross-org outcomes at global scale; elevates safety, responsibility, and culture. Candidate questions (signal-rich): - Which L&V show up most on this team, and how are they measured? - What are the north-star and guardrail metrics for this AI surface? How do you run ramps by city/region? - How does the team approach model observability, on-call, and rollback? - What is the balance between platform investment and product features in the next 2–3 quarters? - How are privacy, fairness, and safety requirements incorporated into the roadmap?