About the Internship
Own the end-to-end recommendation stack for a mobile-first, multilingual product serving 50k+ concurrent users. You’ll design retrieval and ranking, wire real-time signals, and expose low-latency APIsSelected intern’s day-to-day responsibilities include:
1. Own the recs service: Build FastAPI endpoints for candidate retrieval and ranking (cursor pagination, duplicate suppression, simple diversity caps).
2. Retrieval: Implement vector search (e.g., pgvector with HNSW) over content/user/item embeddings.
3. Ranking: Ship pragmatic rankers (logistic regression / LightGBM / small neural re-rankers) with feature engineering from clicks, dwell, recency, and metadata.
4. Signals & features: Consume interaction events (Kafka/Redpanda), maintain real-time features in Redis, and aggregate analytics in ClickHouse.
5. Cold-start & quality: Handle new users/items, balance exploration vs. exploitation, enforce eligibility/editorial filters.
6. Ownership: Write clear docs, set guardrails, and drive a roadmap for relevance and latency improvements.
Number of Openings
2 openingsPerks of this Internship
Certificate 5 days a week Flexible work hours Recommendation letter
Other Requirements
1. are available for the work from home job/internship
2. have relevant skills and interests
3. can start the work from home job/internship between 12th Sep'25 and 17th Oct'25
4. are available for duration of 3 months
5. have already graduated or are currently in any year of study
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