Senior Applied AI/ML Scientist - Search Ranking
$211k - $291k • San Francisco, CA
Posted 18d ago
Job Location
San Francisco, CA
Tech Stack
Remote Work Policy
On-site
Categories
Applied AI Engineer
About the job
As a Senior Applied AI/ML Scientist on the Search team, you will shape the technical vision, machine-learning algorithm strategy, and system design behind Faire's Search functionality. You will advance real-time search and recommendation systems that power next-generation shopping experiences by working 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. This is a chance to influence the end-to-end personalized discovery experience at Faire within a high-scale, deeply multi-modal environment, collaborating closely with a talented team of scientists and engineers.
Responsibilities
- Build next-generation Search ranking algorithms integrating deep learning and machine learning to personalize the retailer discovery journey.
- Leverage LLMs to extract multimodal signals (text, visual) for user profiling and intent understanding.
- Partner with cross-functional teams to experiment and improve ML models for search ranking.
- Design and productionize natural-language search and discovery systems for intelligent agents to generate relevant collections, explain search results, and assist retailers.
- Share best practices in deep learning model development, agent-workflow evaluation, and MLOps, and mentor teammates through code reviews and technical guidance.
Requirements
- 5+ years of industry experience building large-scale ML models and shipping ML solutions to production.
- 3+ years of experience in search, recommendation, or ads ranking.
- Master's or PhD in Computer Science, Statistics, or a related STEM field.
- Strong programming skills in Python, Java, or equivalent.
- Hands-on experience with deep-learning libraries (e.g., PyTorch) and big data technologies (e.g., Spark).
- Deep understanding of machine learning best practices (training/serving, imbalanced data, A/B testing, feature engineering, feature/model selection).
- Deep understanding of algorithms (user modeling, deep learning, reinforcement learning) applied to search, recommendation, and advertising.
- Product-focused mindset with a bias toward execution, moving from research to production.
- Excellent written and verbal communication skills.
- Strong cross-functional influence to raise the technical bar.
Benefits
- Equity
- Health insurance
- Latest enterprise AI tools