Snap currently has 8 active AI-related job listings. The majority of these roles are focused on serving infrastructure, accounting for 38% of the openings, followed by application and post-training stages, each representing 25%. Engineering is the most frequent function for these positions. The company is hiring for these roles in the United States and Austria. Frequent technology tags include model_serving, inference_infra, and recommender_systems. Over the last 30 days, Snap posted 1 new AI role, an 83% decrease compared to the previous 30-day period.
Currently tracking 6 active AI roles, with 56 new openings in the last 4 weeks. Primary focus: Serve · Engineering. Salary range $157k–$235k (avg $196k).
Snap currently has 15 active AI-related roles in our index. The most common open titles are: Computer Architecture Intern, Machine Learning Engineer, Generative ML , Level 5, Machine Learning Engineer, Level 4, Machine Learning Engineer, Level 5, Machine Learning Engineering Intern. Most positions are in Engineering and Product.
Snap's active AI hiring is concentrated in: application (40%), agents (27%), serving infrastructure (20%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Snap is hiring AI talent in: United States (11 roles), United Kingdom (1 role), Australia (1 role).
Job postings at Snap most frequently mention: Ranking & Relevance, Machine Learning, Computer Architecture, TensorFlow, System Design.
In the past 30 days, Snap has posted 5 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Software Engineer, ML Infrastructure, Level 4 Software Engineer, ML Infrastructure at Snap, focusing on scaling ML infrastructure, optimizing training and inference systems, and improving ranking and recommendation systems. The role involves designing and optimizing infrastructure for ML workloads, building feature generation and serving pipelines, developing high-performance inference systems, and managing data for ML training and evaluation. | ServeData | 7 |