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Compare RNNs, LSTMs, Transformers, and MPC

TeslaMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenMachine Learning

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This question evaluates understanding of sequence-modeling architectures (RNNs, LSTMs, Transformers) and Model Predictive Control, assessing architectural choice, training and optimization trade-offs, handling of long-range dependencies, and methods for integrating learned dynamics with control.

Explain how you used RNNs, LSTMs, and Transformers in your project. Compare their capabilities for sequence modeling, training considerations, and when you would choose each. Specify which Transformer architecture you used (encoder-only, decoder-only, or encoder–decoder) and the reasoning. Briefly explain the fundamentals of Model Predictive Control (MPC)—including cost function, constraints, and control horizon—and discuss how MPC could be combined with learning-based models.