Snowflake has 80 active AI-related job listings, with a significant focus on roles within the agents stage, comprising 64% of their openings. The majority of these positions are in Engineering. The company is actively hiring in the United States and Poland. Frequent technology tags include agent_orchestration, llm_observability, and model_serving, suggesting a concentration on developing and deploying AI agents. Over the last 30 days, Snowflake has posted 28 new AI roles, representing a 28% decrease compared to the previous 30-day period.
Data AI · Cloud data warehouse
Currently tracking 57 active AI roles, down 25% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $156k–$380k (avg $250k).
Snowflake currently has 82 active AI-related roles in our index. The most common open titles are: Senior Technical Support Engineer, Observe by Snowflake (3), Senior Data Scientist (2), Senior Security Architect, Applied Field Engineering (AFE) (2), Engineering Manager - SnowConvert AI , AI Engineer - Cortex Code Quality. Most positions are in Engineering and Product.
Snowflake's active AI hiring is concentrated in: agents (61%), application (22%), serving infrastructure (7%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Snowflake is hiring AI talent in: United States (70 roles), Poland (6 roles), Switzerland (2 roles), United Kingdom (1 role).
Job postings at Snowflake most frequently reference: agent orchestration, llm observability, model serving, rag, evals.
In the past 30 days, Snowflake has posted 22 new AI-related roles. That is a -21% change versus the prior 30 days (28 → 22).
| Title | Stage | AI score |
|---|---|---|
| Enteprise Account Executive, Acquisition - Malaysia Enterprise Account Executive for Snowflake in Malaysia, focusing on building net-new business and helping enterprise organizations leverage Snowflake's platform for their cloud and AI ambitions. The role involves identifying, engaging, and closing new logos, creating pipeline, and delivering outcomes for customers by understanding their data and AI transformation needs. | — | 5 |