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Research Intern, User Modeling and Personalization

Snap Inc

Research Intern, User Modeling and Personalization

full-timePosted: Sep 1, 2026Updated: Sep 3, 2026Washington, Bellevue

Job Description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.We are looking for a Research Scientist Intern to join our User Modeling and Personalization Research Team! Our team’s mission is to invent new ways to model user behavior, and empower our business partners to build world-class user-centric ML systems which shape personalized experiences across Snap. Our work spans the domains of structured data modeling, recommendation systems, and large-scale machine learning. Together with you, we seek to redefine the state-of-the-art in technology to deliver our users customized experiences which delight them.What you'll do:Lead research projects in the user modeling and personalization domains (sub-areas include graph modeling, generative recommendation, personalization, and ML efficiency)Build scalable research prototypes and evaluate them in large-scale machine learning scenariosPublish your findings at top conferencesKnowledge, Skills, & Abilities:Strong technical knowledge of state-of-the-art ML algorithms in one or more above sub-areas Demonstrated ability in defining, leading and executing challenging research projectsStrong computer science fundamentals, problem-solving and engineering skillsProven ability to mentor interns, students and junior researchers Minimum Qualifications:Currently enrolled in a PhD program in a technical field such as computer science, machine learning, statistics, mathematics, or equivalent years of experienceTrack record of (co-)first-author publications in top machine learning, data mining, information retrieval or language venues (e.g. KDD, WSDM, SIGIR, RecSys, ACL, ICLR, NeurIPS, COLM etc.)Strong familiarity with ML libraries such as PyTorch, Tensorflow, Jax or related frameworksExperience with distributed (multi-GPU, or multi-node) model training, inference and experimentation; experience with large-scale settings in academic/industrial research labs is a plusPreferred Qualifications:Experience with large-scale machine learning in an academic or industrial research lab, or equivalent open-source experienceExperience with distributed data processing and machine learning frameworks on Enterprise Cloud solutions like Google Cloud, AWS, and/or AzureFamiliarity with language models and generative recommendation and retrievalDemonstrated ability to transform cutting-edge research into tangible product improvementsSpecialization and hands-on experience with state-of-the-art recommendation models and scalable machine learning technologies If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information."Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!CompensationIn the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.Zone A (CA, WA, NYC):The base salary range for this position is $81,000-$121,000 annually. Zone B: The base salary range for this position is $77,000-$115,000 annually.Zone C:The base salary range for this position is $69,000-$103,000 annually.

Locations

  • Washington, Bellevue
  • California, Los Angeles

Salary

81,000 - 121,000 USD / yearly

Skills Required

  • ML libraries such as PyTorchintermediate
  • distributedintermediate
  • large-scale machine learning in an academicintermediate
  • distributed data processingintermediate
  • language modelsintermediate
  • state-of-the-art recommendation modelsintermediate

Required Qualifications

  • Currently enrolled in a PhD program in a technical field such as computer science, machine learning, statistics, mathematics, or equivalent years of experience (experience)
  • Track record of (co-)first-author publications in top machine learning, data mining, information retrieval or language venues (e.g. KDD, WSDM, SIGIR, RecSys, ACL, ICLR, NeurIPS, COLM etc.) (experience)
  • Strong familiarity with ML libraries such as PyTorch, Tensorflow, Jax or related frameworks (experience)
  • Experience with distributed (multi-GPU, or multi-node) model training, inference and experimentation; experience with large-scale settings in academic/industrial research labs is a plus (experience)
  • Currently enrolled in a PhD program in a technical field such as computer science, machine learning, statistics, mathematics, or equivalent years of experience (experience)
  • Track record of (co-)first-author publications in top machine learning, data mining, information retrieval or language venues (e.g. KDD, WSDM, SIGIR, RecSys, ACL, ICLR, NeurIPS, COLM etc.) (experience)
  • Strong familiarity with ML libraries such as PyTorch, Tensorflow, Jax or related frameworks (experience)
  • Experience with distributed (multi-GPU, or multi-node) model training, inference and experimentation; experience with large-scale settings in academic/industrial research labs is a plus (experience)

Preferred Qualifications

  • Experience with large-scale machine learning in an academic or industrial research lab, or equivalent open-source experience (experience)
  • Experience with distributed data processing and machine learning frameworks on Enterprise Cloud solutions like Google Cloud, AWS, and/or Azure (experience)
  • Familiarity with language models and generative recommendation and retrieval (experience)
  • Demonstrated ability to transform cutting-edge research into tangible product improvements (experience)
  • Specialization and hands-on experience with state-of-the-art recommendation models and scalable machine learning technologies (experience)
  • Experience with large-scale machine learning in an academic or industrial research lab, or equivalent open-source experience (experience)
  • Experience with distributed data processing and machine learning frameworks on Enterprise Cloud solutions like Google Cloud, AWS, and/or Azure (experience)
  • Familiarity with language models and generative recommendation and retrieval (experience)
  • Demonstrated ability to transform cutting-edge research into tangible product improvements (experience)
  • Specialization and hands-on experience with state-of-the-art recommendation models and scalable machine learning technologies (experience)
  • "Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. (experience)
  • Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! (experience)

Responsibilities

  • Lead research projects in the user modeling and personalization domains (sub-areas include graph modeling, generative recommendation, personalization, and ML efficiency)
  • Build scalable research prototypes and evaluate them in large-scale machine learning scenarios
  • Publish your findings at top conferences
  • Lead research projects in the user modeling and personalization domains (sub-areas include graph modeling, generative recommendation, personalization, and ML efficiency)
  • Build scalable research prototypes and evaluate them in large-scale machine learning scenarios
  • Publish your findings at top conferences

Benefits

  • general: In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.
  • general: Zone A (CA, WA, NYC):

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