Senior Applied AI/ML Scientist - Search

4+ years

of experience building large-scale ML systems, including

2+ years

in search, recommendation, or ads ranking.

Hands-on experience with deep-learning libraries (e.g. PyTorch) and vector-search infrastructure (e.g. Faiss, ScaNN, Pinecone).

A strong record of productionizing models that blend LLMs (e.g. BERT, GPT-class) with structured features to drive personalization.

California: the pay range for this role is $192,000 to $264,000 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Hybrid Faire employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday).

Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.

Why you’ll love working at Faire

We are entrepreneurs:

As a

Senior Applied AI/ML Scientist

on the Search group, you’ll help shape the technical vision, machine-learning algorithm strategy, and system design behind one of our most important growth levers: Search (think about what you do when you land on any e-commerce site). You’ll advance real-time Search and Recommendation systems that power next-generation shopping experiences.

You’ll work at the frontier of algorithms, combining large language models, natural-language processing, query understanding, deep learning, transformer-based sequential modeling, graph neural networks, and structured behavioral data to return hyper-relevant, personalized products and brands for every user query.

This is a rare chance to influence end-to-end personalization in a high-scale, deeply multi-modal environment while collaborating closely with a talented team of scientists and engineers.