Tesla·Machine Learning Engineer·Technical Phone Screen·Senior

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Jun 2026

Summary

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.

Questions Asked (2)

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.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

This one sprawled.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a spectrum from rule-based to learned policies, emphasizing trade-offs in realism, controllability, and scalability. Then discuss how to handle behavior diversity and reactivity, compare log-replay vs. closed-loop simulation, and finish with concrete evaluation metrics for realism.

Pro tip: At Tesla, simulation is critical for validating Autopilot at scale, so highlight how your approach balances fidelity with computational efficiency and enables targeted scenario generation for edge cases.

1. Define the modeling spectrum

Introduce rule-based methods (e.g., IDM, MOBIL) and learned policies (e.g., imitation learning, RL), noting their strengths and weaknesses in terms of interpretability, data needs, and realism.

2. Address behavior diversity and reactivity

Explain how to inject diversity via parameter randomization, latent variable models, or multi-agent RL, and how to make agents react to the ego vehicle using attention mechanisms or game-theoretic formulations.

3. Compare log-replay and closed-loop simulation

Contrast log-replay (open-loop, non-reactive, but realistic) with closed-loop (reactive, but prone to distribution shift), and propose hybrid approaches like agent-agnostic replay or counterfactual simulation.

4. Propose evaluation metrics for realism

Suggest metrics such as distributional distance (e.g., Wasserstein) on trajectory statistics, collision rates, time-to-collision, and human plausibility studies, and discuss how to validate against real-world logs.

5. Tie back to Tesla's needs

Conclude by emphasizing scalability, coverage of edge cases, and integration with Tesla's data engine for continuous improvement.

Key Points to Mention

Rule-based models (IDM, MOBIL) are interpretable but lack diversity; learned policies (imitation, RL) capture complexity but need large data and can be unsafe.
Behavior diversity can be achieved via parameter sampling, latent space models, or population-based training; reactivity requires modeling interaction (e.g., social pooling, graph neural networks).
Log-replay is open-loop and non-reactive, risking distribution shift; closed-loop enables reactivity but may diverge; hybrid methods like counterfactual replay balance both.
Evaluation should include distributional metrics (e.g., Wasserstein distance on trajectories), safety metrics (collision rate, TTC), and human evaluation for realism.
Scalability and computational efficiency are crucial for large-scale simulation at Tesla; consider parallelization and level-of-detail modeling.
Validation against real-world data and iterative refinement using Tesla's fleet data is key to closing the sim-to-real gap.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

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.

Algorithms & Data Structures

Author's notes

Cleaner than I feared.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and constraints, then propose an efficient solution using a reverse cumulative sum of squared L2 norms. Discuss implementation details, complexity, and potential optimizations or edge cases.

Pro tip: Mention that this operation is common in trajectory prediction and can be vectorized for GPU efficiency, showing awareness of ML deployment contexts.

1. Clarify the problem

Restate the input shape and output requirement, and confirm that the suffix sum is inclusive of the current waypoint. Ask about data types and any constraints.

2. Outline the algorithm

Compute squared L2 magnitudes for each waypoint, then perform a reverse cumulative sum along the waypoint axis to get suffix sums.

3. Discuss implementation

Use vectorized operations (e.g., torch.cumsum with reversed tensor) for efficiency. Provide pseudocode or code snippet.

4. Analyze complexity

State time and space complexity: O(batch * num_waypoints) time, O(batch * num_waypoints) space for output, with potential for in-place operations.

5. Address edge cases and optimizations

Consider empty waypoints, single waypoint, and numerical stability. Mention potential for fused kernels or avoiding extra memory allocations.

Key Points to Mention

Squared L2 magnitude computation: x^2 + y^2
Reverse cumulative sum (suffix sum) along axis=1
Vectorization for GPU efficiency (e.g., using PyTorch or TensorFlow)
Time and space complexity analysis
Handling edge cases like zero waypoints or batch size
Potential for in-place operations or memory optimization

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.