Tesla Machine Learning Engineer interview questions

15 Machine Learning Engineers have shared notes from their Tesla interviews, with the questions they were asked in each round. That is 29% of the 52 Tesla interview notes on Screna AI.

15 Machine Learning Engineer interview notes

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·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·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, 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
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·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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Tesla ML engineer screen, one coding problem focused on physics-based safety logic. Pretty niche for an ML role but I guess autonomous driving means you need to think in real-world units, not just tensors.
  • Write a function in Python that decides whether a vehicle should brake immediately, given current speed, distance to obstacle, maximum deceleration, and reaction time. Return the braking decision, when braking starts, and the expected stopping distance.

“The physics tripped me up more than the code did.”

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Tesla·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Tesla ML Engineer interview that went pretty deep into sequence modeling architectures and control theory. The questions weren't softball stuff, they wanted you to have actually used these models and thought about the tradeoffs, not just recite definitions.
  • Walk through how you've used RNNs, LSTMs, and Transformers in a project. How do they compare for sequence modeling, what are the training differences, and when would you pick one over the others?
  • Which Transformer variant did you use in your project, encoder-only, decoder-only, or encoder-decoder, and why did you make that choice?
  • Explain the core ideas behind Model Predictive Control, covering the cost function, constraints, and control horizon, and then discuss how you'd integrate a learned model into an MPC framework.

“This is the kind of question where you can dig yourself a hole if you try to cover everything at once.”

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Tesla·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026
Tesla ML Engineer interview with a meaty system design question around building an LLM-based arithmetic solver. One question, but it had a lot of layers to unpack and I don't think I covered all of them cleanly.
  • Design an LLM-based system that can solve arithmetic problems across a range of complexity, from simple expressions like 4+5 to things like summing 1 to 100. How do you decide when the model should answer directly versus calling a formula or running code? Walk through your chain logic, tool usage, prompting strategy, guardrails, error handling, and how you'd evaluate and track experiments.

“I started with the routing logic because that felt like the crux of it.”

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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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