Yelp
LLMs for search query understanding and review highlights at local scale
Interview in a few hours? A quick sprint for Yelp
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How Yelp usually runs it for applied AI and LLM engineer roles.
- Focus: Background and role fit
Recruiter screen
A call with someone from recruiting about your background and the role before technical rounds begin.
- Focus: Working code, step by step
Initial technical screen
An early coding round (online challenge or live screen) scored on how many parts of a multi-step problem you complete.
- Focus: Practical coding, data structures
Problem-solving rounds
Onsite coding interviews built from scaled-down versions of real problems Yelp teams have solved, not trivia or memorized algorithms.
- Focus: Architecture and trade-offs
System design
A design interview where you lay out a service or system and talk through its trade-offs.
- Focus: Ownership, teamwork, Yelp values
Values and collaboration interviews
Structured behavioral rounds on ownership, tenacity and curiosity, and on working well with others, scored against a shared rubric.
Topics to prepare for Yelp
The topics its loop weighs most, each with what interviewers check and what to study.
- GenAI system designSystem design
- Behavioral interviews: STAR stories for AI engineersBehavioral
- Python for ML interviewsCoding
- Cost and latency engineering for LLM appsSystem design
- Calling LLM APIs in Python: async, streaming, retriesCoding
- The project deep-dive roundBehavioral
- RAG vs fine-tuning vs prompting: choosing the approachSystem design
- Design an AI customer-support agentSystem design
How it weights your plan
Selecting Yelp when you build a plan multiplies each topic’s priority score:
Official prep and resources
Public candidate write-ups
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