Snap
Consumer · Social
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 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.
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 |
|---|---|---|
| Staff Software Engineer, ML Infrastructure, Level 6 Staff Software Engineer, ML Infrastructure role focused on scaling ML infrastructure, optimizing embedding, feature, and training data storage/compute for massive scale ML models. Responsibilities include designing and optimizing infrastructure systems, developing high-performance embedding generation/batch inference systems, and building data management systems for scalable data collection, labeling, processing, and evaluation. The role also involves integrating ML data quality systems and working with ML engineers to deploy models into production, while utilizing AI tools for development. | ServeData | 7 |
| 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 |