Salesforce
Enterprise · CRM
- HQ
- San Francisco, US
- Founded
- 1999
- Size
- 35,000+
- Website
- salesforce.com
Enterprise · CRM
Salesforce currently has 133 active AI-related job listings. The majority of these roles, 78%, are focused on agents, with application roles making up another 12%. Engineering is the most frequent function hiring for these positions. Frequent tech tags include agent_orchestration, llm_observability, and tool_use, suggesting a focus on building and managing AI agent capabilities.
Currently tracking 106 active AI roles, with 397 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $197k–$345k (avg $271k).
Salesforce currently has 142 active AI-related roles in our index. The most common open titles are: Partner Technical Architect - Agentforce (3), AI Builder, Emerging Talent - Arabic Market (2), AI Builder, Emerging Talent - BNL Market (2), AI Builder, Emerging Talent - French Market (2), AI Builder, Emerging Talent - Hebrew Market (2). Most positions are in Engineering and Product.
Salesforce's active AI hiring is concentrated in: agents (77%), application (11%), serving infrastructure (5%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Salesforce is hiring AI talent in: United States (82 roles), India (12 roles), Ireland (11 roles), Mexico (9 roles).
Job postings at Salesforce most frequently mention: Content Generation, Software Engineering, Agentic Systems, System Design, Python.
In the past 30 days, Salesforce has posted 80 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Principal Data and AI Architect This role is for a Principal Data and AI Architect at Salesforce, focusing on designing and operationalizing enterprise-grade data and AI architectures, specifically around the Agentforce platform and its integration with external AI agents. The role involves pre-sales technical design, shaping best practices for generative AI and agent interoperability, building prototypes, and acting as a technical advisor to customers. It requires expertise in AI/ML concepts, prompt engineering, agent lifecycle management, and integrating various data and AI components within a customer's ecosystem. | Agent | 7 |