Tesla Coding Interview: Autopilot: C++ & ML

The Autopilot and AI org runs Tesla's hardest coding loop. Apply to Perception, Planning, AI Inference, or Dojo and the bar stops being one skill. It becomes four stacked skills, and a single loop probes all of them. You need medium and hard LeetCode, modern C++ depth, GPU and CUDA reasoning, and the ability to write a small ML routine from nothing. A candidate who is fluent in three of the four and hoping the fourth stays quiet tends to get found out.

The Tesla angle

On the Autopilot track a clean LeetCode record is the entry fee, not the interview. The volume track rewards you for solving the medium fast and clean. This one assumes you can already do that, then spends its real minutes elsewhere. An interviewer might hand you a graph or a DP problem to warm up. Then they ask you to reason about ownership and moves in C++, and to sketch how the same computation maps onto thousands of GPU threads. You may also implement an ML primitive like k-means or a convolution with no library to lean on. The four axes are the point. Prepare only the algorithm axis and you are preparing for a different job.

The from-scratch ML asks are not research questions. They are a few nested loops with one detail that carries the grade: the squared-distance assignment in k-means, the inclusive output size in a convolution, the overlap test in a Premium question. Get the small detail exact and these become the easiest points on the hardest track.

TeslaPremium lesson · Coding round

The 3 Tesla problems are below.

A Premium questionCoding · Medium
A Premium questionCoding · Medium
A Premium questionCoding · Hard

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The 3 Tesla problems are below.

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