About the role

As a Senior Applied AI/ML Scientist on the Search 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 will advance real-time search and recommendation systems that power next-generation shopping experiences.

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.

This is a rare chance to influence the end-to-end personalized discovery 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

• 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
• Leverage LLM to extract multimodal signals (text, visual) to better profile users and their intents.
• Partner closely with teams across Faire to experiment and improve the ML models for search ranking and beyond.
• 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.
• Share best practices regarding deep learning model development, agent-workflow evaluation, and MLOps, and help teammates level up through code reviews and technical guidance.

You're a great fit if you have

• 5+ years of industry 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.
• A product-focused mindset and a bias toward execution-moving quickly from research papers to prototypes and production.
• Excellent written and verbal communication skills and strong cross-functional influence that raise the technical bar beyond your immediate team.

Bonus Points

• 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 experience building search and recommendation systems for e-commerce or two-sided marketplaces.
• Experience using AI tools (e.g., Cursor, Claude Code, Codex) for code development and daily productivity.
• Familiarity with Kotlin

Salary Range

US: the pay range for this role is $211,000 to $290,500 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.