This is the kind of question where you can dig yourself a hole if you try to cover everything at once.
Start with a concrete project where you used each architecture, then compare them on sequence modeling capabilities, training dynamics, and computational trade-offs. Conclude with a decision framework for when to choose each, tying it back to Tesla's real-time, resource-constrained environment.
Pro tip: Emphasize that Transformers aren't always the answer—for streaming or low-latency tasks, LSTMs can still win. Show you understand the engineering constraints, not just the theory.
Briefly describe a project where you applied RNNs, LSTMs, and Transformers, highlighting the sequence modeling problem and why you tried multiple architectures.
Compare them on sequence modeling: RNNs struggle with long-term dependencies, LSTMs mitigate vanishing gradients with gates, Transformers use self-attention for parallel processing and global context.
Discuss training: RNNs/LSTMs are sequential and slow to train, prone to vanishing gradients; Transformers train in parallel but need large data and compute, and use techniques like learning rate warmup.
Explain when to pick each: RNNs for simple, short sequences; LSTMs for moderate sequences with limited data or streaming needs; Transformers for long sequences, large datasets, and tasks requiring global context.
Tie choices to Tesla's context: real-time inference on vehicle hardware, sensor fusion, and the need for efficiency—highlighting trade-offs between accuracy, latency, and memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Caught me a bit flat-footed because I'd been treating 'Transformer' as one monolithic thing in my head.
Start by clearly stating which Transformer variant you used (encoder-only, decoder-only, or encoder-decoder) and briefly describe the project. Then explain the reasoning behind your choice by linking the task requirements (e.g., input-output structure, need for bidirectional context, generation) to the architectural strengths and trade-offs of that variant.
Pro tip: Quantify the impact of your choice with metrics (e.g., latency, accuracy, memory) and mention if you experimented with other variants before settling on this one, showing a data-driven and iterative approach.
Clearly name the Transformer variant used and give a one-sentence summary of the project, including the task (e.g., classification, generation, translation).
Describe the key characteristics of the task that influenced your choice, such as whether it required understanding full context, generating sequences, or mapping input to output.
Explain how the chosen variant's architecture (e.g., bidirectional attention, causal masking, cross-attention) aligns with the task requirements and why it is more suitable than the alternatives.
Acknowledge the limitations or trade-offs of your choice (e.g., computational cost, training complexity) and briefly mention why other variants were less appropriate.
Share the outcomes (e.g., performance metrics, efficiency gains) that validated your choice, and if possible, mention any experiments comparing variants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Did not see this coming in what felt like an ML interview.
Start by clearly defining MPC's core components—cost function, constraints, and control horizon—and explain how they work together to solve an optimization problem at each timestep. Then, transition to integrating a learned model by discussing how to train it (e.g., neural networks) and incorporate it into the MPC loop, while addressing challenges like uncertainty and real-time performance. Emphasize Tesla's context by highlighting safety, reliability, and computational efficiency.
Pro tip: Show awareness of the trade-offs between model accuracy and computational cost, and mention techniques like approximate MPC or learning-based MPC to handle real-time constraints on embedded hardware.
Explain the cost function as the objective to minimize (e.g., tracking error and control effort), constraints as physical or safety limits, and control horizon as the number of future control moves optimized.
Describe how MPC solves an optimization problem at each timestep, applies the first control input, and repeats with updated state measurements, highlighting its receding horizon nature.
Discuss how a learned model (e.g., neural network) can replace or augment the traditional physics-based model, and outline training using system identification or reinforcement learning.
Explain the integration: use the learned model for prediction within the MPC optimization, and address challenges like model uncertainty, computational load, and safety guarantees.
Mention techniques to ensure real-time feasibility (e.g., model quantization, approximate MPC) and safety (e.g., fallback to safe controllers, uncertainty quantification).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.