Samsara·Machine Learning Engineer·Technical Phone Screen
Jun 2026
Samsara ML engineer loop was heavily LLM-focused, way more than I expected for what I thought would be a general ML role. The questions went deep on pretraining, alignment, and production concerns all in one shot.
- Walk through how transformer-based large language models are pretrained, and then how finetuning works on top of that.
- Compare instruction tuning and RLHF as alignment approaches. What are the tradeoffs?
- What strategies would you use to reduce hallucinations in an LLM system?
- How would you evaluate an LLM? Talk through metrics like perplexity, task-specific accuracy, and human evaluation.
- How would you monitor an LLM once it's deployed in production?
- What inference optimizations would you apply to an LLM in production? Things like quantization or KV caching.
“I went pretraining to finetuning in a pretty linear way, next-token prediction, massive corpora, then supervised finetuning on labeled data.”