Tesla AI Engineer Case Interview: End-to-End Camera-Centric Perception and Data Engine under Real-Time Constraints

What this case covers: a first-principles, end-to-end plan to improve a camera-centric perception capability (e.g., lane and drivable-space understanding with occupancy-style outputs) for a production vehicle platform, delivered under strict on-vehicle latency, memory, and power constraints. The session mirrors Tesla’s fast-paced, hands-on style: minimal prompts, deep technical pushback, focus on measurable impact, and bias for action. Structure (what the interviewer will ask you to do): - Problem framing: clarify success metrics and constraints. Define a concrete target (e.g., reduce long-tail perception failures in rain and nighttime construction) and specify latency, memory, and power budgets appropriate for an embedded inference stack. Call out safety gates and rollback requirements. - Data engine design: propose how to mine hard examples from fleet data, auto-label at scale, triage false positives and false negatives, and prioritize long-tail scenarios. Include active learning loops, shadow-mode triggers, and simulation or re-synthesis to cover rare events while avoiding distribution drift. - Model approach: outline architecture choices for multi-camera temporal perception (e.g., feature fusion across time, ego-motion handling, calibration hygiene). Compare alternative backbones and heads, justify loss functions, and discuss calibration, uncertainty estimates, and how you would validate improvements statistically. - Systems and performance: sketch how you will meet strict real-time budgets on an automotive-grade compute platform. Cover quantization, pruning, operator fusions, caching, input resolution trade-offs, batching strategy (often single-frame online), and how you will profile hotspots. Address thermal and power considerations and how they influence design. - Evaluation and safety: define offline and on-road metrics beyond mAP and IoU, including latency P95/P99, energy per inference, false-negative cost in safety-critical contexts, and scenario coverage metrics. Propose an A/B plan with shadow mode, canary rollout, safety monitors, and immediate rollback triggers. - Deployment plan: describe the path from experiment to production, including dataset versioning, model registries, reproducible training, on-vehicle validation, OTA delivery considerations, and telemetry to verify real-world gains. Tesla-specific interview dynamics you should expect: - First-principles pressure-testing: you will be asked why each choice is the simplest thing that can work and what you would ship in 2–3 weeks versus the ideal 3–6 month plan. - Do more with less: interviewer may cut your compute budget mid-discussion and ask you to re-plan while holding safety and accuracy targets. - Hands-on reasoning: you may be asked to sketch pseudo-code for a hard-example mining sampler or an efficient pre/post-processing pipeline, and to back-of-the-envelope the latency and memory of your design. - Ownership mindset: expect follow-ups about failure analysis, on-call implications after deployment, and how you would respond to a live regression within hours. What success looks like: - Clear, quantified problem statement and metrics; credible plan to reduce specific failure modes. - Sound data strategy paired with simple, robust modeling choices that respect embedded constraints. - Concrete profiling and optimization tactics; understanding of calibration, uncertainty, and safety gates. - Pragmatic rollout with shadow testing, canaries, and telemetry; crisp trade-off reasoning when constraints change. Red flags the interviewer watches for: - Hand-wavy references to generic large models without an embedded inference plan. - Over-reliance on manual labeling without automated mining and triage loops. - Ignoring latency, memory, or power budgets; lack of rollback and safety considerations. - Inability to prioritize a scrappy first ship that measurably improves the fleet. Candidate materials provided during the case: a brief problem statement, a few sample failure clips, and basic platform constraints. You are expected to ask clarifying questions, derive missing numbers with back-of-the-envelope math, and justify assumptions.