Machine Learning Engineer – Search & Retrieval Systems
New
W
WizardE-commerce
Remote - USAFull-TimeMiddle
Salary225000 - 280000 USD per year
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Job Details
- Experience
- 5–8+ years
- Required Skills
- PythonElasticSearchLLM
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: 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
Responsibilities
- 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
- Design and productionize the learning-to-rank system – from feature engineering through model training to production deployment and A/B evaluation
- Build the search feedback loop – instrument and integrate behavioral signals into ranking and retrieval
- 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
- 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
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