Ford Motor Company AI Engineer Case Interview: Predictive Maintenance and Edge ML for Connected Vehicles

What this case covers (Ford-specific): A realistic, end-to-end problem centered on Ford’s connected vehicle ecosystem (e.g., F‑150, Transit, Bronco) and Ford Pro commercial fleets. You will design an AI-driven predictive maintenance solution that leverages on-vehicle signals (CAN/OBD, BMS, DTCs), telematics uplinks, warranty and service history, and dealer network operations—balancing safety, quality, cost, and the constraints of automotive-grade hardware. The session reflects Ford’s culture of safety-first engineering, cross-functional collaboration with hardware/vehicle systems teams, and a pragmatic delivery mindset aligned to production programs and OTA update cycles. Case prompt: “Design a predictive maintenance system to reduce unplanned downtime for Ford Pro Transit vans used by last‑mile delivery fleets. Part of the inference must run at the edge (in-vehicle) with intermittent connectivity; models must be updatable OTA; alerts should integrate with dealer service scheduling and parts availability. Your approach must respect functional safety boundaries for non-safety vs. safety-adjacent features, and comply with data privacy and regulatory expectations.” Expected artifacts during the interview: - Problem framing: measurable business and customer outcomes (e.g., % reduction in roadside events, warranty cost avoidance, technician first‑time‑fix), target components (brakes, battery, drivetrain), and definitions for precision/recall vs. false-alarm cost to customers/dealers. - Data plan: signal list (e.g., wheel speed variance, brake pad wear indicators, SOC/SOH from BMS, coolant temps, charging patterns), sampling rates, edge vs. cloud feature computation, handling model-year and trim variability, and a plan for data governance with dealer/warranty data. - Modeling approach: time-series anomaly detection plus survival/RUL modeling; candidate algorithms (gradient boosting, temporal CNN/RNN/Transformer, Bayesian survival), uncertainty estimation, and calibration for field reliability across climates and duty cycles. - Edge + cloud architecture: on-vehicle resource assumptions (limited CPU/GPU, memory, power), feature windows, quantization/pruning, fallback behavior when models are unavailable, and cloud pipeline for training, evaluation, and staged rollout. - MLOps and quality: CI/CD with safety gates, dataset versioning, drift/decay monitoring by model-year and geography, A/B or phased OTA rollout, rollback strategy, and dealer feedback loop via service codes. - Safety and compliance considerations: boundaries with functional safety (e.g., ISO 26262-aligned hazard thinking for advisory features), SOTIF-like reasoning for misuse/edge cases, cybersecurity posture, and privacy-by-design for customer and fleet data. - Operational integration: alert-to-action flow into FordPass Pro/fleet dashboards, dealer capacity constraints, parts logistics, and success metrics reported to program leadership. Format and flow (reflecting Ford’s interview style): - 10 min: Clarifying questions and alignment on objectives and constraints (safety, quality, cost, timing aligned to a product program). - 35–40 min: Whiteboard/system design and modeling deep dive (edge vs. cloud trade-offs, feature store, model selection, validation strategy, and OTA rollout plan). - 10 min: Stakeholder roundtable simulation (vehicle systems engineer, dealer ops, data privacy), assessing trade-offs and communication. - 5–10 min: Wrap-up with risks, phased roadmap (pilot → limited OTA → fleet-wide), and measurable acceptance criteria. What interviewers probe for at Ford: - Systems thinking across hardware, software, and vehicle operations; ability to quantify customer and dealer impact. - Sound model choices for noisy, non-stationary vehicle data; treatment of uncertainty, drift, and generalization across model-years. - Practical edge ML optimizations and clear rollback/failsafe strategies consistent with safety-first culture. - MLOps rigor suitable for production vehicles and OTA governance. - Collaboration and communication with non-ML stakeholders; clarity, humility, and data-driven decision making.