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We power commerce through conversation by enabling brands to sell, market, and engage their customers—all via text.
Machine Learning Engineer – Search & Retrieval Systems
Location
United States
Posted
60 days ago
Salary
$225K - $280K / year
Seniority
Senior
Job Description
Machine Learning Engineer – Search & Retrieval Systems
Wizard
• 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
Job 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
- Equity in the form of stock options
- Medical, dental, and vision coverage
- 401(k) plan
- Flexible PTO and company holidays
- Fully remote work within the United States
- Periodic company offsites and team gatherings
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