12 days ago
Responsibilities
- Lead the development of a scalable search cluster for millions of documents.
- Deploy learning-to-rank models to optimize relevance using behavioral signals.
- Build and scale robust Entity Recognition pipelines for enhanced document understanding.
- Architect next-gen search infrastructure for dynamic document corpora and real-time indexing.
- Drive improvements in query construction, indexing, and search performance.
- Stay updated with the latest search and indexing technologies.
- Collaborate with product and applied research teams to translate user needs into search innovations.
- Produce clean, scalable code and influence system architecture across the relevance and platform stack.
Requirements
- Bachelors/Masters/PhD in Statistics, Mathematics, Computer Science, or a related field.
- 7+ years of backend engineering experience with 3+ years in search or information retrieval.
- Strong proficiency in Python.
- Hands-on experience with search engines like Opensearch or Elasticsearch.
- Strong understanding of information retrieval concepts and modern neural search techniques.
- Experience with text processing, NLP, and relevance tuning.
- Familiarity with relevance evaluation metrics such as NDCG, MRR, and MAP.
- Experience with large-scale distributed systems.
- Strong analytical and problem-solving skills.
- Excellent communication abilities and a collaborative mindset.