Machine Learning Engineer, Generative ML, Level 5

Snap
CA, USA / WA, USA / NYC, USA2026-08-19Full time

About the job

Snap’s Generative ML Platform team builds cutting-edge AI technologies that power creative, scalable experiences for hundreds of millions of Snapchatters worldwide. From multimodal LLMs and video generation to real-time AR, human understanding, and 3D content creation, we develop the full stack of generative AI, including foundational models, efficient infrastructure, and on-device and server-side inference. Our team creates intuitive tools, platforms, and agentic systems that empower creators, developers, and internal teams to bring ideas to life, while advancing personalized, human-centric experiences across mobile, web, and wearable devices like Spectacles. We're looking for a Machine Learning Engineer to join our Generative ML team!

Responsibilities

Develop innovative machine learning technology and products that serve millions of Snapchatters

Work on state of the art generative pipelines for image, video, language or audio generation

Deliver generative machine learning experiences on device

Build cutting-edge augmented reality experiences using generative and diffusion models

Partner with cross-functional Snap teams to explore and prototype new products

Qualifications

Minimum

Bachelor’s Degree in a technical field such as computer science, mathematics, statistics or equivalent years of experience

5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience

Research or engineering experience in one or more of the following: generative models, efficient models, diffusion models, language models, or other related applications of machine learning

Preferred

Master's degree or PhD in a related technical field

Experience developing real-time software for mobile applications

Knowledge of GenAI, especially image, video, language and audio generation foundations

Knowledge of efficient model foundations

Track record of successful projects in GenAI field

Examples of your work such as open source projects, blog posts, Kaggle contests, top conference or journal publications, etc.