Tesla Behavioral Interview – AI Engineer (First‑Principles, Ownership, and Impact)

This behavioral interview for Tesla AI Engineers probes first‑principles thinking, extreme ownership, and the ability to deliver production ML systems under tight constraints. Expect rapid, direct questioning that drills into a few projects end‑to‑end: defining the problem from fundamentals, scoping scrappy MVPs, iterating with real‑world data, and quantifying impact (latency, accuracy, safety KPIs, cost). Interviewers focus on how you operated with limited resources, moved fast without compromising safety in a high‑stakes environment (e.g., autonomy/perception, planning, data engines, labeling, on‑device inference), and how you handled failure modes and edge cases. You will be asked to walk through concrete decisions, trade‑offs, and hands‑on contributions—not just leadership—covering data acquisition and quality, labeling strategy, model/feature choices, deployment on embedded or HPC stacks, monitoring, incident response, and post‑launch iteration. Collaboration topics include partnering with hardware, controls, and manufacturing teams, communicating crisply, and challenging assumptions with data. The style is blunt and detail‑oriented; expect follow‑ups like “how exactly did you measure that?” and “what did you personally build?” with requests for metrics, experiments, and root‑cause analyses.