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Tesla

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·Backend Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for a backend engineer role at Tesla. Two questions, both pretty standard conflict and impact stuff, but Tesla tends to probe harder than you'd expect on the follow-ups.
  • Describe a time you had a conflict with a teammate or another team. What happened, how did you handle it, and what came out of it?
  • What's a project you're most proud of, and what kind of impact did it have?

“I had a decent story ready but fumbled the resolution part.”

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Tesla·Software Engineer·Onsite - Coding / Algorithms

Jul 2026
Tesla SWE coding round, one graph problem the whole time. Not the hardest interview I've done but I definitely fumbled my way through the BFS part before it clicked.
  • You have an n x n grid of 0s and 1s representing water and land. The grid contains exactly two islands. Find the minimum number of water cells you need to convert to land to connect the two islands.

“Took me a minute to even figure out how to separate the two islands first.”

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Tesla·Backend Engineer·Technical Phone Screen

Jul 2026
Tesla backend interview with a tree problem that looks clean on the surface but has a few gotchas worth thinking through. Nothing too wild, but the BFS-from-target angle tripped me up a bit.
  • Given a binary tree and a target node that starts burning at time 0, fire spreads each minute to adjacent nodes (left child, right child, parent). How many minutes does it take for the whole tree to burn?

“The spread-to-parent part is what gets you.”

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Tesla·Software Engineer·Technical Phone Screen

Jul 2026
Tesla SWE interview that was heavier on stats than I expected. One question, but it had a lot of layers and I wasn't fully prepared for the multiple comparisons piece.
  • You have historical quality data for batteries randomly assigned to one of 5 groups. Each battery goes through 3 binary (pass/fail) quality tests. How would you determine which group has the best overall quality in a statistically rigorous way? Cover how you'd define a combined quality metric, handle unequal group sizes, quantify uncertainty, and deal with the multiple comparison problem.

“I started with the composite metric part and that went okay.”

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Tesla·Software Engineer·Online Assessment (OA)

Jul 2026
Tesla SWE interview that was basically a gauntlet of classic algorithm problems back to back. Nine questions total, covering everything from string parsing to graph traversal. Felt more like an OA than a conversation.
  • Implement an expression evaluator that parses a string of digits and operators (+, -, *, /) with spaces and returns the integer result. No parentheses. Division truncates toward zero. Do it in a single pass.
  • Given an array of positive integers, count how many index triplets (i < j < k) form the sides of a valid non-degenerate triangle. Aim for O(n^2) after sorting.
  • Merge a list of closed intervals, combining any that overlap or share an endpoint, and return the result sorted.
  • Count the number of connected land clusters in a grid of '1's and '0's, where connectivity is 4-directional.
  • Find the smallest missing positive integer in an unsorted array. Must run in O(n) time and O(1) extra space.
  • Return the maximum sum of any non-empty contiguous subarray. Optionally return the subarray's start and end indices.
  • Given an array of daily stock prices, find the maximum profit from a single buy followed by a single sell. Return 0 if no profitable trade exists.
  • Find the length of the longest sequence of consecutive integers in an unsorted array. Target average O(n) using hashing.
  • Given a number of courses and prerequisite pairs, determine if all courses can be completed. If yes, return a valid ordering. Handle up to 100,000 nodes and edges.

“The single-pass constraint is what makes this annoying.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Tesla ML engineer interview that went deep into statistical foundations, specifically importance sampling in a lot of detail. One question, but it had a lot of moving parts and they clearly expected you to derive things on the spot, not just recite definitions.
  • Explain importance sampling: derive an estimator for E_p[f(X)] when sampling from a proposal distribution q, show both the unnormalized and self-normalized weight formulations, discuss bias and variance tradeoffs, explain what makes an ideal proposal distribution, define effective sample size, walk through a concrete numerical example, and cover failure modes like weight degeneracy and heavy-tailed proposals.

“This was basically a full lecture compressed into one interview question.”

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Tesla·Software Engineer·Technical Phone Screen

Jul 2026
Tesla data engineering interview, one SQL question that looks straightforward until you actually sit down and think about what 'one pass' means. Pretty focused technical screen, no fluff.
  • Given an orders table with columns order_id, status, amount, and date, write a single SQL query that computes in one pass: total revenue for completed orders per date, count of pending orders per date, and count of cancelled orders per date.

“The 'one pass' constraint is where it gets interesting.”

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Tesla·Product Manager·Hiring Manager Screen

Jul 2026
Interviewed for a PM role at Tesla and got hit with a classic pressure-test question about timelines. Short loop, pretty direct.
  • If you were told you had to ship a product or feature in half the originally planned timeline, what would you do?

“My first instinct was to start listing things I'd cut, which I think was the wrong entry point.”

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Tesla·Software Engineer·Technical Phone Screen

Jul 2026
Tesla software engineer interview that went pretty deep on HTTP fundamentals. One question but they really wanted you to know the details, not just rattle off method names.
  • Walk me through the HTTP request methods (GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS). Which do you actually use day-to-day, and can you explain idempotency, safety, and common pitfalls?

“Started okay naming the methods but then they asked me to explain idempotency and I fumbled the distinction between safe and idempotent.”

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Tesla·Software Engineer·Technical Phone Screen

Jul 2026
Tesla SWE technical screen, two coding questions back to back. Both were more involved than I expected for a phone round, lots of edge case discussion and they pushed on time/space complexity pretty hard.
  • Given a list of integers with length n, check whether it's a permutation of [0, 1, ..., n-1]. You must do it in-place with O(1) extra space, no hash sets, no sorting, and aim for O(n) time.
  • Implement a power function that computes base raised to an integer exponent without using any library exponentiation. It needs to run in O(log |exp|) time, handle negative exponents, zero cases, and the minimum 32-bit integer value for the exponent.

“I went straight to XOR and they seemed okay with that direction, but then they asked me to also handle out-of-range values and duplicates explicitly and I fumbled a bit.”

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Tesla interview FAQ

What rounds are in the Tesla interview?

Candidates most often report a technical phone screen (37 write-ups), a system design round (6), a hiring manager screen (3) and a product sense round (2).

What does the Tesla technical phone screen cover?

Across 37 technical phone screen write-ups, the most tagged question topics are Technical Trade-offs (50 questions), Algorithms & Data Structures (33) and System Design (22).