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Tesla interview notes, page 4 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

Jun 2026
Tesla ML Engineer technical screen focused pretty heavily on RL theory. Two meaty topics, not much small talk, felt more like a grad seminar than a job interview.
  • Walk me through the differences between GRPO and PPO, covering how each estimates advantages, handles clipping, uses (or avoids) a value function, and when you'd pick one over the other.
  • How do you decide between designing a reward function by hand versus training a reward model, and what factors push you toward one or the other?

“This is where I spent most of my mental energy.”

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Tesla·Software Engineer·Technical Phone Screen

Jun 2026
Tesla SWE interview where they had me implement a task scheduler from scratch, runnable and everything. Not a design question, they wanted actual working code with a test showing tasks firing at the right times.
  • Implement a runnable task scheduler that lets clients add tasks at any time, where each task has a callback and a scheduled time. Tasks must not run before their scheduled time, must run in chronological order, and the scheduler should keep running and accept new tasks while active. Include a working example demonstrating correct timing.

“This took me a minute to even figure out where to start.”

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Tesla·Backend Engineer·Technical Phone Screen

Jun 2026
Tesla backend engineer interview with a handful of short technical fundamentals questions. Nothing too wild but the breadth was a bit all over the place, jumping from Kafka to browser internals to storage engines in the same session.
  • In a Kafka-based batch consumer, how do you ensure a batch isn't processed more than once?
  • Walk through everything that happens, end to end, when a user types a query into Google and hits search.
  • If the API gateway for a large web service goes down, how should the system handle it gracefully?
  • What storage engine or database would you pick for storing and searching documents, and why?
  • What's the difference between a compiler and an interpreter?

“This one tripped me up a bit.”

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Tesla·Backend Engineer·Onsite - System Design / Architecture

Jun 2026
Tesla backend engineering interview focused entirely on system design, three problems back to back, all pretty demanding. The questions leaned heavily into distributed systems thinking and real-world reliability concerns rather than toy examples.
  • Design a reserved-seat ticketing platform where users browse a venue map, select seats, hold them temporarily, and complete checkout. How do you prevent double booking under high concurrency, and how does the API and frontend handle seat state?
  • Design a system that ingests periodic transaction files from a bank and a payment processor, compares them against internal records, and flags missing, duplicate, or mismatched entries for operators or downstream systems.
  • Design a remittance file processing system that validates each transaction, enriches it with additional data, stores the result, and triggers notifications. Cover how you'd handle retries, idempotency, partial failures, and overall reliability.

“The concurrency piece is where this gets interesting and also where I fumbled a bit.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Two-part round at Tesla for an MLE role: one open-ended design discussion on agent behavior modeling in driving simulation, followed by a coding problem on trajectory tensors. The coding part was more straightforward than I expected, but the discussion question had a lot of surface area and I felt like I only scratched it.
  • How would you model the behavior of other agents (vehicles, pedestrians, etc.) in a driving simulation? Walk through rule-based approaches versus learned policies, how you'd handle behavior diversity and reactivity to the ego vehicle, log-replay versus closed-loop simulation, and how you'd evaluate whether the simulated behavior is realistic.
  • Given a tensor of shape [batch, num_waypoints, 2] representing (x, y) trajectory points, compute the suffix sum of squared L2 magnitudes along the waypoint axis. The output shape should be [batch, num_waypoints], where each entry at index i holds the sum of squared magnitudes from waypoint i to the end.

“This one sprawled.”

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Tesla·Software Engineer·Technical Phone Screen

Jun 2026
Tesla SWE interview that went deep on a classic algorithm problem. They wanted the full arc: brute force first, then walk up to linear time, then squeeze out the extra space. Not a vibe check, more like a whiteboard lecture you're giving to yourself.
  • Implement a solution to the Trapping Rain Water problem. Start with a brute-force approach, explain its time and space complexity, then optimize to O(n) time with O(n) space, and finally discuss how you'd get it down to O(1) space.

“The brute force part was fine, nested loops, nothing to it.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Tesla ML Engineer interview, got a coding question that was basically 'implement conv2d from scratch, no torch, no nothing.' Pretty technical for a phone screen but I guess that's on brand for them.
  • Implement the forward pass of a 2D convolution from scratch, without using any deep learning library functions. Given an input tensor in NCHW format, filter weights, an optional bias, stride, and padding parameters, compute the output tensor correctly.

“I knew the math well enough but translating it into clean indexing code under pressure is a different thing.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Tesla ML engineer interview, technical screen focused entirely on low-level NumPy implementation. No fluff, just code and tradeoffs. The kind of round where you really feel the gap between knowing something conceptually and actually writing it out.
  • Implement a 2D convolution from scratch using only NumPy, supporting NCHW input format, configurable stride and padding, and weights of shape (C_out, C_in, k_h, k_w). Start with a nested-loop reference implementation, then optimize it using vectorization (like im2col or stride tricks), and explain the time and memory tradeoffs between the two approaches.

“This took me way longer than I expected to set up correctly.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Tesla ML Engineer interview focused entirely on reinforcement learning for autonomous driving, moving from policy optimization theory to reward design philosophy to a live coding problem. Pretty rigorous, they clearly wanted someone who could reason about failure modes, not just implement the happy path.
  • What is the difference between PPO and GRPO in the context of modern RL and RLHF-style training? Cover the objective each optimizes, how each handles advantage estimation, and when you'd pick one over the other.
  • When would you choose a hand-engineered heuristic reward versus a learned reward model trained from human preferences? What are the tradeoffs?
  • Given batched 2D trajectories of shape [batch, num_waypoint, 2] sampled at 10 Hz, implement a vectorized speed-limit penalty in two variants: one that penalizes time spent exceeding the limit, and one that penalizes the magnitude of the excess.
  • What goes wrong with your constant speed-limit reward if the actual limit is time-varying, say 50 mph for the first two seconds then 30 mph for the next three?
  • More broadly, what happens when the speed limit changes but isn't part of the agent's observation? How do you fix it?
  • If you had to keep a constant-limit reward for legacy reasons, how would you detect at training time that the agent is being trained against a hidden time-varying objective?
  • How would you extend the speed penalty to also discourage harsh braking near a speed limit boundary, without double-counting the existing penalty?

“I knew PPO cold but GRPO tripped me up a bit.”

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Tesla·Software Engineer·Technical Phone Screen

Jun 2026
Tesla data engineering interview with a recursive SQL problem that sounds straightforward until you're actually writing it out under pressure.
  • Given an org table with employee_id, manager_id, and name columns, write a query to find all direct and indirect reports under a specific manager at every level of depth. Return employee_id, manager_id, name, the depth level, and the full hierarchy path.

“Knew immediately it was a recursive CTE problem but fumbled the anchor member the first time.”

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