Staff Research Scientist (User Modeling and Personalization)

TL;DR

Staff Research Scientist (User Modeling and Personalization): Inventing new ways to model user behavior and building world-class user-centric ML systems with an accent on generative modeling, recommendation systems, and information retrieval. Focus on translating cutting-edge research into scalable prototypes and tangible product improvements for millions of users.

Location: Must be based in Bellevue, USA (Office 4+ days per week)

Company

A leading technology company focused on visual communication and innovation through Snap Research.

What you will do

• Formulate and derive research agendas in user modeling, generative modeling, and recommendation systems.
• Partner with engineering teams to translate research into real-world ML applications used by millions of users.
• Build and evaluate scalable research prototypes in large-scale machine learning scenarios.
• Mentor interns, students, and junior researchers.
• Publish findings at top-tier computer science and ML conferences.

Requirements

• PhD in Computer Science, Machine Learning, language technologies, or a related technical field.
• 5+ years of industry or postdoctoral experience.
• Track record of publications in top venues (e.g., ICLR, NeurIPS, ICML, KDD, RecSys).
• Experience with distributed (multi-node and multi-GPU) ML model training and inference.
• Strong proficiency in Python and PyTorch.
• Commitment to a "default together" approach, working in the office 4+ days per week.

Nice to have

• Experience with large-scale data processing using Google Cloud, AWS, or Azure.
• Familiarity with post-training, preference optimization, and large-scale search/recommendation data.
• Demonstrated ability to transform research into tangible product improvements.

Culture & Benefits

• Collaborative environment emphasizing in-person interaction to build culture and reinforce values.
• Comprehensive medical coverage and emotional/mental health support programs.
• Paid parental leave.
• Competitive compensation packages with long-term success sharing.