Snap
Consumer · Social
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 11 active AI roles, with 56 new openings in the last 4 weeks. Primary focus: Ship · Engineering. Salary range $147k–$259k (avg $200k).
Consumer · Social
Snap currently has 16 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 (38%), agents (25%), serving infrastructure (25%). 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 (12 roles), United Kingdom (1 role), Australia (1 role).
Job postings at Snap most frequently mention: Machine Learning, Ranking & Relevance, Computer Architecture, TensorFlow, PyTorch.
In the past 30 days, Snap has posted 7 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Computer Vision Engineer Computer Vision Engineer for Snap's Spectacles team, focusing on developing and deploying novel ML/CV algorithms for next-generation AR glasses. | Post-train | 8 |
| Machine Learning Engineering Intern Machine Learning Engineering Intern to join the Spectacles AR engineering team, focusing on scene understanding for AR experiences. The role involves prototyping, training, and evaluating ML models for computer vision and multimodal understanding, contributing to geometric scene understanding, 3D reconstruction, semantic scene understanding, visual localisation, and connecting scene understanding to language for AR interactions. The intern will partner with mentors and cross-functional teams to integrate work into production-facing systems. | Post-trainAgent |
| 8 |