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. Snap Research serves as an innovation engine for the company. Our projects range from solutions to hard technical problems that significantly enhance Snap’s existing products, to riskier explorations that can lead to fundamental paradigm shifts in the way people communicate and express themselves. The team consists of scientists and engineers who experiment with and invent new technology that has a lasting impact on Snap’s products. We also frequently publish our work at top conferences and journals in computer science and related fields. We are looking for a Research Scientist 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 generative and language models for information retrieval, efficient large-scale recommender systems, and representation learning for structured graph data. 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: Formulate and derive a research agenda in the user modeling and personalization domains, including generative modeling, recommendation systems, information retrieval, and efficiency Partner with engineering teams to translate research to business impact for real-world ML applications used by millions of Snapchatters Build scalable research prototypes and evaluate them in large-scale machine learning scenarios Share your expertise with teammates and interns Publish your findings at top conferences Knowledge, Skills, & Abilities: Strong technical knowledge of machine learning, information retrieval, personalization, language and state-of-the-art deep learning literature Demonstrated ability in defining, leading and executing challenging research projects Strong computer science fundamentals, problem-solving and engineering skills (Python, PyTorch) Pragmatic, hands-on approach to research with a drive to build working prototypes rather than solely rely on theoretical exploration Proven ability to mentor interns, students and junior researchers Minimum Qualifications: PhD in computer science, machine learning, language technologies or related technical field such as statistics, mathematics, or equivalent years of experience 5 years of industry or postdoctoral experience Track record of publications in top machine learning, information retrieval or language venues (e.g. ICLR, NeurIPS, ICML, KDD, RecSys, SIGIR, WSDM, ACL, COLM, etc.) Experience with distributed (multi-node and multi-GPU) ML model training, inference and experimentation Experience applying language models in the context of generative search, ranking and/or personalization Preferred Qualifications: Experience with large-scale machine learning problems in an academic or industrial research lab, or equivalent open-source experience Experience with large-scale data processing, collection or synthesis using machine learning frameworks on Enterprise Cloud solutions like Google Cloud, AWS, and/or Azure Familiarity with post-training, preference optimization, working with large-scale search or recommendation interaction data, and recommender systems Demonstrated ability to transform cutting-edge research into tangible product improvements 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! Compensation 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. Zone A (CA, WA, NYC) : The base salary range for this position is $229,000-$343,000 annually. Zone B : The base salary range for this position is $218,000-$326,000 annually. Zone C : The base salary range for this position is $195,000-$292,000 annually. This position is eligible for equity in the form of RSUs.

Requirements

Must-haves

Formulate and derive a research agenda in the user modeling and personalization domains, including generative modeling, recommendation systems, information retrieval, and efficiency
Partner with engineering teams to translate research to business impact for real-world ML applications used by millions of Snapchatters
Build scalable research prototypes and evaluate them in large-scale machine learning scenarios
Share your expertise with teammates and interns
Publish your findings at top conferences
Strong technical knowledge of machine learning, information retrieval, personalization, language and state-of-the-art deep learning literature
Demonstrated ability in defining, leading and executing challenging research projects
Strong computer science fundamentals, problem-solving and engineering skills (Python, PyTorch)
Pragmatic, hands-on approach to research with a drive to build working prototypes rather than solely rely on theoretical exploration
Proven ability to mentor interns, students and junior researchers
PhD in computer science, machine learning, language technologies or related technical field such as statistics, mathematics, or equivalent years of experience
5 years of industry or postdoctoral experience
Track record of publications in top machine learning, information retrieval or language venues (e.g. ICLR, NeurIPS, ICML, KDD, RecSys, SIGIR, WSDM, ACL, COLM, etc.)
Experience with distributed (multi-node and multi-GPU) ML model training, inference and experimentation
Experience applying language models in the context of generative search, ranking and/or personalization

Nice-to-haves

Experience with large-scale machine learning problems in an academic or industrial research lab, or equivalent open-source experience
Experience with large-scale data processing, collection or synthesis using machine learning frameworks on Enterprise Cloud solutions like Google Cloud, AWS, and/or Azure
Familiarity with post-training, preference optimization, working with large-scale search or recommendation interaction data, and recommender systems
Demonstrated ability to transform cutting-edge research into tangible product improvements
If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information .

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningpythonawsazure

Platform: workday
Effort: long

Benefits

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Parental Leave