Rejected after the Tesla onsite last spring. Got generic feedback from the recruiter. Spent the next three weeks doing what I always do: reconstruct what probably happened and figure out what I'd do differently.
For context: Senior SWE, 14 years in backend, distributed systems, went for an Autopilot data pipeline role.
Round by round breakdown of what I think went wrong:
Coding (LeetCode-style, 2 rounds): I thought these went fine. One was a graph problem, one was something with heaps. Solved both. In retrospect I think my solutions were correct but I was slow to get there. Tesla's bar on the coding rounds is apparently 'yes and fast.' Speed matters more than I expected for senior-level.
System design: This is where I think I lost it. The prompt was vague: design a system to process and store telemetry data from vehicles at scale. I went deep on the distributed queue architecture, Kafka-style, partitioning strategy, consumer groups. Spent most of the 45 minutes there. The interviewer asked several questions about on-device processing and edge constraints that I had under-indexed. I realized in the car home that the real design problem was as much about what you process on the vehicle before sending as what you do in the cloud. That's a Tesla-specific hardware-software tradeoff and I came in too cloud-native.
Behavioral: Felt okay. They asked about scope, ambiguity, disagreement with a manager. I had stories for all of these. Maybe not Tesla-flavored enough (I referenced big-tech-style processes they probably don't have).
What I'd change: Study Tesla's specific technical constraints before the system design round. The edge/cloud tradeoff is central to almost everything they build. Be faster in coding rounds. Practice on a timer, not just for correctness. Frame behavioral stories around speed, constraint, and scrappiness rather than process and consensus.
Still bitter. But it made me sharper.