Swiggy
Food and quick-commerce delivery; ML for ETAs, ranking, ads and recs
Interview in a few hours? A quick sprint for Swiggy
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How Swiggy usually runs it for applied AI and LLM engineer roles.
- Focus: ML-from-scratch coding, SQL
Coding round (Python + SQL)
Hands-on coding that can include building a basic ML algorithm from scratch in Python/NumPy plus a practical SQL query.
- Focus: ML and stats fundamentals
Technical round with a DS lead
A data science lead discusses your current work and tests ML, probability and evaluation-metric fundamentals.
- Focus: Project depth, applied ML design
Resume deep-dive and case study
Interviewers check the models and metrics on your CV, then give an open recommendation or ads-matching case to design end to end.
- Focus: Culture and role fit
HR / hiring manager round
A closing conversation on fit and expectations; campus loops report a light HR round at the end.
How it weights your plan
Selecting Swiggy when you build a plan multiplies each topic’s priority score:
Official prep and resources
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