AI Session-Based Recommendation Model Developer

About the role

Responsibilities

• Design transformer-based session recommendation architectures modeling within-session interaction sequences
• Implement hybrid models combining real-time session signals with available long-term profile context
• Build session intent inference identifying whether a session is in browse, compare, or purchase mode
• Develop cold-start handling for users with zero interaction history before current session
• Create serving infrastructure delivering session recommendations under 50ms latency
• Monitor session recommendation lift against non-personalized fallbacks as primary quality metric

Requirements

• 6+ years in recommender systems or ML engineering with session modeling or sequential recommendation experience
• Deep understanding of attention-based sequence models and their application to recommendation
• Strong Python and PyTorch skills with real-time ML serving at scale
• Experience with cold-start recommendation strategies and new user acquisition traffic
• Track record demonstrating measurable recommendation quality improvements on cold-start cohorts

Benefits

• Fully remote with flexible schedule
• Comprehensive health benefits
• $4,000 annual professional development budget
• Equity participation
• Performance bonus tied to conversion lift metrics

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