Staff Applied ML/AI Scientist - Search

2 months ago

San Francisco, CA, USA or New York, NY, USAStaff+

H1B Sponsor

Base Salary

$231k - $318k/yr

Responsibilities

Own the next-generation Search engine, integrating LLMs, query understanding, dense vector retrieval, personalization embeddings, multi-stage ranking, and reinforcement learning for personalized feeds under 100ms latency.
Design and productionize natural-language search and discovery systems, including intelligent agents for personalized collections, result explanations, browsing, filtering, and evaluation.
Lead model development and GPU-based deployment efforts using frameworks such as Triton.
Mentor and grow senior Applied Scientists and machine-learning engineers.
Establish best practices for model development, agent workflow evaluation, and MLOps.

7+ years of experience building large-scale machine-learning systems, including 3+ years in search, recommendation, or ads ranking.
Hands-on experience with deep-learning libraries such as PyTorch and vector-search infrastructure such as Faiss, ScaNN, or Pinecone.
Experience productionizing models that combine LLMs such as BERT or GPT-class models with structured features for personalization.
Strong Python skills and experience operating reliable systems in high-stakes environments.
Excellent communication and cross-functional influence.
Open-source ML library contributions or peer-reviewed ML/AI publications are advantageous.
An MS or PhD in Computer Science, Statistics, or a related STEM field is advantageous.
Strong practices around model development, agent workflow evaluation, and MLOps are advantageous.

Hybrid schedule with in-office work 3 days per week on Tuesdays, Thursdays, and one flex day on Monday, Wednesday, or Friday.
Hybrid employees may work remotely up to 4 weeks per year.
Eligible for equity and comprehensive benefits.
Access to enterprise AI tools and opportunities for professional growth.

1,001-5,000 employees

Faire is a wholesale technology platform empowering brands and retailers to strengthen the unique character of local communities.