Senior Applied ML/AI Scientist - Search
$211k - $291k • New York City, NY; San Francisco, CA
Posted 2mo ago
Job Location
New York City, NY; 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 combining query understanding, deep learning, transformer-based sequential modeling, graph neural networks, and structured behavioral data to deliver hyper-relevant, personalized products and brands. This role offers a unique opportunity to influence the end-to-end personalized discovery experience in a high-scale, multi-modal environment, collaborating with a talented team of scientists and engineers.
Responsibilities
- Build next-generation Search ranking algorithms using deep learning and machine learning to personalize the retailer discovery journey.
- Leverage LLMs to extract multimodal signals (text, visual) for improved user profiling and intent understanding.
- Partner with cross-functional teams to experiment and enhance ML models for search ranking.
- Design and productionize natural-language search and discovery systems for intelligent agents to generate personalized collections and assist retailers.
- Share best practices in deep learning model development, agent-workflow evaluation, and MLOps, and provide technical guidance to teammates.
Requirements
- 5+ years of industry experience building and shipping large-scale ML models to production, with 3+ years 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 like PyTorch and big data technologies like Spark.
- Deep understanding of machine learning best practices (training/serving, imbalanced data, A/B testing, feature engineering, selection) and 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 with strong cross-functional influence.
Benefits
- Equity
- Health insurance
- PTO
- Latest enterprise AI tools