Research Scientist, Language for Personalization

About Snap Inc.

Snap Inc. is a technology company driven by the belief that the camera enhances human communication and enriches lives. Through its core products – Snapchat, Lens Studio, and Spectacles – Snap empowers self-expression, living in the moment, learning, and shared fun. Snap Research serves as a vital innovation engine, tackling challenging technical problems to improve existing products and exploring groundbreaking ideas that could redefine how people interact.

The User Modeling and Personalization Research Team

Join our User Modeling and Personalization Research Team, where our mission is to pioneer new methods for understanding user behavior. We empower our partners to develop world-class user-centric machine learning systems that craft personalized experiences across Snap. Our work encompasses generative and language models for information retrieval, efficient large-scale recommender systems, and representation learning for structured graph data. We aim to redefine the state-of-the-art to deliver delightful, customized experiences to users.

What You'll Do

• Lead impactful research projects within the user modeling and personalization domains, including generative modeling, recommendation systems, information retrieval, and efficiency.
• Build scalable research prototypes and rigorously evaluate their performance in large-scale machine learning environments.
• Share your advanced expertise and insights with teammates and interns, fostering a collaborative learning environment.
• Publish your significant findings at premier conferences, contributing to the broader scientific community.
• Partner closely with engineering teams to seamlessly integrate your innovative technology for millions of Snapchatters.

Knowledge, Skills, & Abilities

• Possess strong technical mastery in machine learning, information retrieval, personalization, and cutting-edge deep learning literature.
• Demonstrated capability in defining, leading, and successfully executing challenging research projects from conception to completion.
• Exhibit strong computer science fundamentals, exceptional problem-solving acumen, and robust engineering skills, particularly in Python and PyTorch.
• Maintain a pragmatic, hands-on approach to research, prioritizing the development of working prototypes over solely theoretical exploration.
• Proven ability to mentor interns, students, and junior researchers, guiding their professional and technical development.

Minimum Qualifications

• A PhD in a relevant technical field such as computer science, machine learning, or mathematics, or equivalent extensive practical work experience.
• Strong familiarity with PyTorch and hands-on experience with distributed (multi-node and multi-GPU) machine learning model training, inference, and experimentation.
• Direct experience applying language models within the contexts of generative search, ranking methodologies, and/or personalization systems.
• A track record of high-quality publications (first-author preferred) in top-tier machine learning, information retrieval, or language venues (e.g., ICLR, NeurIPS, ICML, KDD, RecSys, SIGIR, WSDM, ACL, COLM).

Preferred Qualifications

• Demonstrated experience with large-scale machine learning problems in either an academic or industrial research laboratory setting, or through equivalent open-source contributions.
• Proficiency in large-scale data processing, collection, or synthesis utilizing machine learning frameworks on Enterprise Cloud solutions such as Google Cloud, AWS, and/or Azure.
• Familiarity with post-training optimization, preference optimization techniques, and working with extensive search or recommendation interaction data.
• Proven ability to translate cutting-edge research directly into tangible product improvements and user benefits.

"Default Together" Policy at Snap Inc.

Snap Inc. practices a “default together” approach, expecting team members to work in an office 4+ days per week to foster culture, reinforce values, and enhance collaboration.

Key skills/competency

• Machine Learning
• Deep Learning
• Natural Language Processing
• Recommendation Systems
• Information Retrieval
• User Modeling
• Generative Models
• PyTorch
• Distributed Systems
• Research Leadership