Snap
Consumer · Social
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).
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 |
|---|---|---|
| Working Student - Machine Learning Working student thesis project focused on developing efficient on-device ML models for AR glasses, specifically exploring event-based sensing and processing combined with deep learning techniques to meet strict latency, energy, and bandwidth constraints on embedded hardware. The role involves designing, prototyping, and evaluating models, exploring efficiency techniques, and demonstrating proof-of-concepts on AR hardware. | ServePost-train | 8 |