Compare RNNs, LSTMs, Transformers, and MPC

Q: Compare RNNs, LSTMs, Transformers, and MPC

This is a Machine Learning interview question from Tesla for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

Sequence Modeling Architectures and MPC (Technical Screen)

You worked on a sequence-modeling project involving multivariate time-series signals and multi-step prediction/control. Address the following:

Architecture usage
- Explain how you used RNNs, LSTMs, and Transformers in your project (what the task was, why each model was chosen, and what changed across iterations).
Capability and training comparison
- Compare RNNs, LSTMs, and Transformers for sequence modeling: handling long-range dependencies, data/computation needs, latency, and robustness.
- Discuss training considerations: optimization, stability, batching/streaming, masking, teacher forcing vs. autoregression, and regularization.
Transformer design choice
- Specify which Transformer architecture you used (encoder-only, decoder-only, or encoder–decoder) and why it fit your task.
MPC fundamentals
- Briefly explain Model Predictive Control (MPC): cost function, constraints, prediction/control horizons, and the receding horizon principle.
Combining MPC with learning
- Discuss how MPC can be integrated with learning-based models (e.g., learned dynamics, uncertainty handling, imitation/distillation, safety).

Compare RNNs, LSTMs, Transformers, and MPC

Sequence Modeling Architectures and MPC (Technical Screen)

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