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# Applied ML/AI Scientist

- **Company:** Hybrid Faire
- **Location:** New York, United States (Remote available)
- **Experience:** Expert
- **Salary:** $211,000.0 - $290,500.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Intrusion Detection Systems, Python (Programming Language), Search Algorithms, Language Modeling, Open Source Technology, Recommender Systems, Reinforcement Learning, Large Language Models, Production Code, Search Engines, Machine Learning Operations, Marketplace
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-applied-ml-ai-scientist-search-faire-8679924

## About the Role

We're looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours., * 3+ years building production ML systems, with meaningful time in search, recommendations, or another retrieval-and-relevance domain.

 * Shipped models that served real traffic, and owned the experiments that proved (or disproved) their value.
 * Depth somewhere in the modern retrieval stack - dual encoders and ANN serving, LLM-based query understanding, learning-to-rank fundamentals - and the ability to pick up the rest.
 * Strong Python and the engineering chops to take your own models to production.
 * Clear communication with scientists, engineers, and PMs: you make a crisp case for your ideas and update quickly when someone has a better one.
 * Excellent product judgment to connect customer and business context to technical decisions

Bonus Points

 * Marketplace or e-commerce experience
 * Publications, open-source work, or public writing on search and recommender systems.
 * MS or PhD in CS, Statistics, or a related field.

## Description

Search is how retailers do their jobs on Faire. Wholesale queries and retailer expectations look different from consumer e-commerce and the right product depends on the store's category, price point, and aesthetic. When we get it wrong, it costs real money.

The Search algorithms team owns everything between the click on the search bar and the final ranker: typeahead and empty-state suggestions, query understanding, retrieval across five-plus independent sources, relevance modeling, and result-page surfaces like carousels and refinements. Within our scope, scientists own components outright: when you own query understanding here, you own the models, the roadmap, and the metrics.

You'll work across the full modern search stack: transformer-based embedding retrieval serving live traffic, LLMs powering query understanding and query rewriting, fine-tuned vision-language models scoring relevance, and graph-based retrieval - with generative retrieval and semantic IDs on the horizon.

What you'll do

 * Contribute to the next-generation Search engine, integrating LLMs, query understanding, dense vector retrieval, deep personalization embeddings, multi-stage ranking, and reinforcement learning to serve personalized product feeds with <100ms latency.
 * Own one or more search components end to end: problem framing, modeling, production code, experiment design, and the call on what to build next.
 * Ship to live traffic and let A/B tests, not opinions, settle what works.
 * Raise the team's bar through design reviews, pairing, and honest post-mortems


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