MLE interview at SpaceX done. Posting this because the ML interview landscape varies wildly by company and SpaceX is an unusual case.
First thing to know: SpaceX does applied ML, not research ML. If you're coming from an NLP or LLM-focused background, expect to talk about how you'd apply models in a production engineering context, not about fine-tuning or architecture novelty. The org cares about whether your model works reliably in a real system, not whether it achieves SOTA on a benchmark.
The rounds:
Coding screen: standard medium-difficulty algorithm question. Nothing ML-specific. They want to confirm you can code. Python, straightforward.
ML system design: This is the main event. I was asked to design an anomaly detection system for time-series sensor data. Open-ended. They wanted me to talk through: data pipeline design, feature engineering approach, model selection rationale, how I'd evaluate it, and how I'd handle concept drift over time. The "handling concept drift" piece got serious attention. They pushed hard on monitoring and retraining strategies.
ML depth interview: More technical. Questions on gradient boosting vs deep learning for tabular data (be ready to have an actual opinion, not a diplomatic non-answer), regularization techniques and when each one matters, how you'd debug a model that performs well in offline eval but poorly in production.
Behavioral: Standard. Come with stories about shipping under constraints, working with messy data, communicating uncertainty to stakeholders.
What they didn't ask about: LLMs, transformers, anything NLP. No graph neural networks. No RL (somewhat surprising). This is a very applied, mission-critical systems context.
Prep advice: be sharp on time-series modeling, anomaly detection methods (statistical and ML-based), and production ML systems. Know the difference between a model that works and a model you can trust in a safety-critical environment. That framing matters a lot at SpaceX.