Machine Learning Engineer – Search & Retrieval Systems

Location: Remote - USA

Department: AI & Machine Learning

About Wizard

Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.

The Role

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.

What You’ll Do

• 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

What Success Looks Like

• You ship ranking and retrieval improvements that measurably move product quality metrics – accuracy, NDCG, latency
• The search pipeline adapts its behavior based on query context rather than relying on static configuration
• You own systems end-to-end: from the training data pipeline through model training to production serving and evaluation
• You build infrastructure that other engineers can extend – clean APIs, config-driven behavior, well-documented evaluation
• Behavioral signals from search flow back into ranking and retrieval, making the system measurably smarter over time

Ideal Background

• 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

Compensation & Benefits

The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.

In addition to base salary, Wizard offers:

• 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

Wizard is committed to fair, transparent, and competitive compensation practices.