Senior Applied AI/ML Scientist - Marketplace Quality
$211k - $291k • Remote • San Francisco, CA
Posted 22d ago
Job Location
San Francisco, CA
Tech Stack
Remote Work Policy
Fully remote
Categories
Applied AI Engineer
About the job
Faire is seeking a Senior Applied AI/ML Scientist to join their Marketplace Quality team. In this role, you will be responsible for developing and implementing algorithmic solutions to ensure the trustworthiness of Faire's marketplace for brands and retailers. This involves matching Faire's catalog against external data, detecting pricing and policy violations, and optimizing actions based on finite resources. You will work with structured and unstructured data, employing techniques like entity resolution, information extraction, multi-modal LLMs, and constrained optimization. The position requires driving projects from conception to production and measurement, in close collaboration with product, engineering, and operations teams.
Responsibilities
- Own applied ML projects end-to-end, from problem framing to model shipping and impact measurement.
- Build and improve pricing-integrity models to detect violations by comparing Faire listings against external pricing signals.
- Solve large-scale product matching and entity resolution problems using text and image embeddings, retrieval, and multi-modal LLMs.
- Extract structured attributes from unstructured listing content (descriptions, images, third-party sources).
- Design targeting and prioritization logic to rank violations by expected marketplace impact within a constrained budget.
- Build human-in-the-loop systems with marketplace operations partners for audits, label generation, and model refinement.
- Design and analyze experiments for enforcement levers and measure their effect on retailer trust and marketplace GMV.
- Partner with product, engineering, operations, and analytics teams to deliver models into production.
- Address challenging problems specific to a two-sided marketplace.
Requirements
- 3+ years of industry experience applying machine learning to real-world problems.
- Experience with relevant business problems such as e-commerce, marketplaces, catalog quality, search, or personalization.
- Experience with relevant technical methods including deep learning, LLMs, computer vision, information extraction, entity resolution, ranking, and/or experimentation.
- Strong programming skills.
- Willingness to learn new tools and techniques.
- Ability to drive projects end-to-end with limited supervision.
- Strong communication skills and ability to work in a cross-functional team.
Benefits
- Equity
- Benefits