Research Engineer, User Modeling and Personalization

Snap Inc.•Bellevue, WA

1d•Onsite

About The Position

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’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles. 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 Engineer 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.

Requirements

Strong engineering fundamentals and experience writing high‑quality, reliable, and maintainable code in Python, demonstrated through industry, open‑source, or substantial academic projects.
Applied machine learning engineering experience: taking models from idea to production, including training, evaluation, debugging, and iteration on large-scale data.
Familiarity with modern sequence and language models, and how they are used for search, ranking, and personalization experiences.
Experience designing and operating ML systems and data pipelines (training, inference, serving, monitoring, and cost/performance tradeoffs).
Understanding of recommendation and information retrieval problems, and how user modeling fits into these ecosystems.
Ability to collaborate with research scientists and engineers to translate research ideas into robust, maintainable production systems.
Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience.
3+ years of industry experience of applying ML in production settings; or Master’s degree in a technical field + 1 year of industry machine learning experience; or PhD in a relevant technical field.
Strong track record of impact through research publications and/or deployed ML systems in areas such as recommendation, information retrieval, or language technologies.

Nice To Haves

Advanced degree in computer science, machine learning, language technologies, or a related technical field (e.g., statistics, mathematics).
Strong coding skills and computer science fundamentals, including data structures, algorithms, and debugging for production-scale systems.
Hands-on experience training and deploying models including distributed training and inference using modern ML frameworks (PyTorch, and/or Tensorflow).
Experience integrating sequence or language models into user-facing products, or relevant academic/research experience.
Experience working on large-scale recommendation, ranking, or search systems in an academic or industrial setting.
Experience building and operating ML pipelines on cloud infrastructure (e.g., GCP, AWS, or similar), including data processing (e.g., Hadoop, Spark, and/or Dataflow) and experimentation workflows (e.g., MLflow, Kubeflow, Vertex AI, or SageMaker).
Demonstrated ability to bridge research and engineering: turning advanced ML ideas into reliable, efficient, and maintainable production system.

Responsibilities

Lead and support research engineering projects in the user modeling and personalization domains, including generative modeling, recommendation systems, information retrieval, and efficiency.
Partner with researchers and other engineers to prototype, experiment, and ship products.
Publish your findings at top conferences.
Partner with engineering teams to deliver your technology to millions of Snapchatters.
Share your expertise with teammates and interns.