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Tesla interview notes, page 2 of 6

Large Enterprises

Most of these Tesla write-ups come from Software Engineers (28), then Machine Learning Engineers (15) and Backend Engineers (5). The most reported rounds are the technical phone screen (37), the system design round (6), the hiring manager screen (3) and the product sense round (2).

52 interview notes · updated Jul 2026

Tesla·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Tesla ML engineer coding round, one problem the whole session. Felt pretty clean on the surface but there were a few moments where I second-guessed myself on the edge case handling.
  • Given a batch of 2D waypoint trajectories with shape [B, N, 2], compute for each batch item and each waypoint the remaining path length from that waypoint to the end of the trajectory. The output should have shape [B, N], where each value is the suffix sum of Euclidean distances between consecutive waypoints. Must run in O(B·N) time and handle edge cases like a single waypoint.

“The suffix sum part clicked fast, you just iterate backwards and accumulate segment lengths.”

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Tesla·Software Engineer·Technical Phone Screen

May 2026
Tesla data engineering interview, one technical SQL question that looks straightforward until you actually sit down to write it. The monthly reset part is where things get interesting.
  • Given a table with columns for date, store ID, and sale amount, write a SQL query that calculates cumulative sales per store, but the running total resets at the beginning of each month. Return store ID, date, and the cumulative amount.

“The reset-per-month part tripped me up.”

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Tesla·Product Manager·Hiring Manager Screen

May 2026
Tesla PM interview, got asked about physical products which felt like a warmup but ended up being the whole vibe of the conversation.
  • What is your favorite physical product?

“Stumbled a bit because I wasn't expecting something so open-ended right out of the gate.”

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Tesla·Software Engineer·Technical Phone Screen

May 2026
Tesla SWE interview that went deep into concurrency. One meaty design problem about synchronizing producer threads to form valid water molecules, no fluff, just straight into the technical weeds.
  • You have an unbounded stream of 'H' and 'O' atoms arriving as callable actions from separate threads. Design a synchronization mechanism that groups them into valid water molecules, releasing exactly two H actions and one O action together per molecule. No busy-waiting allowed. Explain correctness in terms of safety, liveness, and scalability, and provide pseudocode.

“This one took me a minute to even parse.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

May 2026
Tesla ML Engineer interview focused almost entirely on how you'd design and validate agent behavior in a driving simulation. Pretty deep technical territory and the questions were interconnected, so if you fumbled the first one the rest got harder fast.
  • How would you model the behavior of other traffic participants in a driving simulator so that the ego agent gets realistic and robust training?
  • What are the main approaches to modeling agent behavior in simulation, like log replay, rule-based systems, or learned policies, and what are the tradeoffs around realism, controllability, and distribution shift?
  • How would you validate that your agent modeling approach is actually good enough to support downstream training and evaluation of the ego agent?

“I started with log replay because it felt safe, then walked into the obvious problem myself: replayed agents don't react to the ego vehicle at all, so you get these ghost-car situations.”

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Tesla·Software Engineer·Technical Phone Screen

May 2026
Tesla SWE interview that leaned heavily on classic algorithm problems. The trapping rainwater question came up and they wanted more than just a working solution, they wanted the full progression from naive to optimal.
  • Given an array of non-negative integers representing bar heights, calculate how much water gets trapped between the bars after rain. Walk through a brute-force solution first, analyze its complexity, then optimize to O(n) time and O(1) space.

“This is the classic trapping rainwater problem and I knew it, which was both good and bad.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

May 2026
Tesla ML Engineer technical screen, basically one massive coding/design question that covered implementing a full Transformer block from scratch with no autograd. The depth they expected was pretty intense for a single session.
  • Implement scaled dot-product attention and a full Transformer block from scratch without using any autograd library. Include both forward and backward passes for the attention module, where the backward for softmax is given to you. Handle multi-head splitting, causal masking, residual connections, LayerNorm, and a feed-forward sublayer.
  • For autoregressive training with teacher forcing, define the next-token cross-entropy loss, explain where the causal mask is applied in the attention computation, and compute perplexity from the loss.
  • Implement finite-difference gradient checks on small tensors to verify your backward pass implementation, and discuss numerical stability considerations like stabilized softmax using log-sum-exp.

“This was the whole interview basically.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

May 2026
Tesla ML engineer interview, technical phone screen that went deep fast. One question, very algorithmic, felt more like a geometry/CS fundamentals test than anything ML-specific.
  • Given a polyline defined by N 2D coordinates, a list of M step distances, and a threshold t, simulate walking along the polyline by advancing each step distance from the current position. After each step, find the nearest vertex within t miles and return its index (or None). Implement this efficiently using NumPy, avoiding naive O(N*M) scanning, and handle edge cases like zero-length segments, repeated points, d=0, and empty input. Discuss time and space complexity.

“This one took me a minute to even parse.”

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Tesla·Software Engineer·Technical Phone Screen

May 2026
Tesla data engineering interview, one technical question that was basically a full pipeline design problem. They gave you a messy CSV scenario and wanted to see how you'd actually clean it end to end, not just name-drop pandas.
  • You're given a CSV of customer transactions with several data quality issues: inconsistent date formats, currency symbols embedded in amount fields, customer IDs with stripped leading zeros, and payment method values that have typos and inconsistent casing. Write a data cleaning pipeline to handle all of this, and describe how you'd validate the data after cleaning.

“Spent probably too long on the date parsing piece because there were multiple formats mixed in the same column and I kept second-guessing whether to use a strict parser or try-except fallbacks.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

May 2026
Tesla ML engineer screen, one technical question about conv layer parameter counts. Pretty focused, no fluff.
  • For a 2D convolution layer with given input channels, output channels, kernel dimensions, stride, padding, and an optional bias, derive the total number of learnable parameters both with and without bias.

“I knew the formula but fumbled explaining it cleanly under pressure.”

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