Robinhood
Senior
Fine-tune and productionize LLMs and agentic workflows for Robinhood's core products
This role builds AI inference services and fine-tuned LLMs for real product use cases at Robinhood, spanning training/fine-tuning (LoRA, RLHF), high-performance inference optimization, RAG pipeline engineering, and shipping model endpoints as production microservices. The interview should test hands-on depth across the full model lifecycle plus the influence and mentorship expected of a senior IC shaping strategic initiatives.
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What this interview tests
- LLM fine-tuning (LoRA, RLHF, prompt optimization)
- Inference optimization (quantization, pruning, distillation)
- RAG pipeline design (embeddings, vector indexes, chunking)
- Production model-serving microservices and API design
- AI evaluation frameworks (hallucination detection, regression testing)
Common question themes
Walk me through a fine-tuning project you led, including your evaluation approach
How do you decide between quantization, pruning, or distillation for a latency-constrained inference service
Describe a RAG pipeline you built — chunking strategy, embeddings, and how you validated retrieval quality
How have you productionized a fine-tuned model as an inference microservice
Tell me about a time you built an automated eval framework to catch hallucinations or regressions before shipping
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