research
Posted Mar 26Senior Applied AI/ML Scientist - Search
at Faire
San Francisco, United StatesHybrid
Responsibilities
- Build our next-generation Search ranking algorithms by integrating the latest advances in deep learning and machine learning to personalize the retailer discovery journey at Faire
- Design and productionize natural-language search and discovery systems so that intelligent agents can generate relevant and personalized collections, explain search results, and assist retailers with browsing, filtering, and evaluation.
Requirements
- At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe.
- As a Senior Applied AI/ML Scientist on the Search ranking team, you will help shape the technical vision, machine-learning algorithm strategy, and system design behind one of our most important growth levers: Search (the primary tool used by customers on any e-commerce site).
- You’ll work at the frontier of algorithms, combining 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.
- experience at Faire within a high-scale, deeply multi-modal environment, while collaborating closely with a talented team of scientists and engineers. What you'll do
- Partner closely with teams across Faire to experiment and improve the ML models for search ranking and beyond.
- experience building large-scale ML models with business impact and shipping ML solutions to production, including 3+ years in search, recommendation, or ads ranking
- A Master’s or PhD in Computer Science, Statistics, or a related STEM field.
- Strong programming skills (Python, Java, or equivalent) and hands-on
- experience with deep-learning libraries (e.g., PyTorch) and big data technologies (e.g., Spark).
- Deep understanding of machine learning best practices (e.g., training/serving, imbalanced data, A/B testing, feature engineering, and feature/model selection) and algorithms (e.g., user modeling, deep learning, and reinforcement learning) with applications in search, recommendation, and advertising domains.
- Contributions to open-source ML libraries or peer-reviewed publications in ML/AI. Industry
- experience developing and productizing LLM-based applications and systems in the search domain. Industry