Machine Learning Engineer – Search & Retrieval Systems

Wizard

1d•$225,000 - $280,000•Remote

About The Position

Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust. We're looking for a Machine Learning Engineer to own the search and retrieval systems that power Wizard's AI shopping agent. Every product recommendation starts with finding the right candidates from millions of listings – fast, relevant, and adapted to the query at hand. You'll own how we retrieve, rank, and adapt search behavior, from the retrieval pipeline through ranking models to the business logic that shapes the final result set. Unlike traditional search engineering – our search pipeline adapts its behavior per query, learns from production signals, and serves a conversational agent where intent evolves across turns. You'd own both the systems that execute search and the applied ML that makes those systems smarter over time.

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

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 (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