Own and evolve the hybrid search pipeline – lexical retrieval, dense vector search, reciprocal rank fusion, and multi-stage reranking on Elasticsearch
Build and train adaptive retrieval models – lightweight classifiers and ranking models that configure search behavior per query, per category, per context (source routing, per-attribute boost prediction, filter mode decisions)
Design and productionize the learning-to-rank system – from feature engineering through model training (LightGBM, ONNX) to production deployment and A/B evaluation
Build the search feedback loop – instrument and integrate behavioral signals (CTR, conversions, add-to-cart) into ranking and retrieval as features for LTR, reward signals for adaptive retrieval, and inputs for search-side personalization
Build the business and ordering layer – separating organic relevance from sponsored/partner placement with quality gates, slot allocation, campaign configuration, and an auction-style approach as the system matures
Own the offline enrichment pipeline – LLM-based product enrichment at scale, data quality monitoring, and index management
Instrument and evaluate everything – bulk evaluation pipelines, per-category metric tracking, regression detection, experiment analysis
Integrate query understanding outputs into retrieval – translating extracted attributes, intents, and constraints into filters, boosts, and retrieval strategy decisions

Requirements

5–8+ years of experience building and shipping search, retrieval, or ranking systems in production
Strong experience with Elasticsearch or similar search engines (Solr, Vespa, OpenSearch) – index design, query optimization, hybrid retrieval
Hands-on experience with learning-to-rank (LightGBM, XGBoost, LambdaMART) or similar applied ranking approaches
Strong Python skills and software engineering fundamentals – clean, typed, well-structured production code
Experience with embeddings and vector search – dense retrieval, ANN indexing, embedding fine-tuning
Pragmatic ML sensibility: you pick the simplest model that works, measure rigorously, and ship iteratively
Experience with offline evaluation methodology – nDCG, MRR, precision/recall at k, A/B test design and interpretation

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ElasticsearchLightGBMXGBoostLambdaMARTPythondense retrievalANN indexingfeature engineeringA/B testingindex design

Soft Skills

pragmatic ML sensibilityiterative shippingrigorous measurement