Databricks currently has 122 active AI-related job listings. The majority of these roles are focused on agents, accounting for 37% of the total, followed by serving infrastructure at 25% and application at 24%. Engineering is the dominant function, with 106 listings. The company is primarily hiring in the United States, with 80 roles, and India, with 12 roles. Frequent technology tags include model serving, agent orchestration, and inference infrastructure. In the last 30 days, Databricks has posted 25 new AI roles, representing a 39% increase compared to the previous 30-day period.
Currently tracking 70 active AI roles, with 152 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $130k–$425k (avg $219k).
Databricks currently has 131 active AI-related roles in our index. The most common open titles are: AI Engineer - FDE (Forward Deployed Engineer) (10), Resident Solutions Architect - Financial Services (8), Senior Solutions Architect - Lakewatch (5), Staff Backend Software Engineer- (AI Platform) (4), Data & AI Platform Architect (Professional Services) (3). Most positions are in Engineering and Product.
Databricks's active AI hiring is concentrated in: agents (39%), serving infrastructure (24%), application (23%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Databricks is hiring AI talent in: United States (83 roles), India (12 roles), United Kingdom (8 roles), Germany (5 roles).
Job postings at Databricks most frequently reference: model serving, agent orchestration, rag, inference infra, llm observability.
In the past 30 days, Databricks has posted 30 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Senior Engineering Manager, AI Runtime Senior Engineering Manager to lead a team responsible for the AI Runtime (AIR) product and its foundational infrastructure, focusing on training and fine-tuning deep learning and LLM models with on-demand GPUs for enterprise customers. This role involves defining roadmaps, driving architectural decisions, and ensuring scalability, extensibility, and performance of GPU training infrastructure. | DataPost-train | 8 |