Zendesk has 37 active AI-related job listings, with a significant focus on roles related to agents, accounting for 46% of their openings. Engineering is the most represented function, with 28 positions. The company is actively hiring in Portugal and Poland. Technical tags frequently appearing include model_serving, llm_observability, and agent_orchestration, suggesting a focus on deploying and managing AI models for customer service applications. Over the last 30 days, Zendesk has added 35 new AI roles, representing a 67% increase compared to the previous 30-day period.
Currently tracking 18 active AI roles, down 73% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $158k–$374k (avg $261k).
Zendesk currently has 29 active AI-related roles in our index. The most common open titles are: Staff Software Engineer (3), Senior Product Manager (2), Staff Machine Learning Engineer (2), AI Agent Abuse Prevention Engineer, AI Services Consultant II. Most positions are in Engineering and Product.
Zendesk's active AI hiring is concentrated in: agents (52%), application (21%), post-training (10%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Zendesk is hiring AI talent in: Portugal (12 roles), Poland (6 roles), United States (5 roles), Ireland (3 roles).
Job postings at Zendesk most frequently reference: llm observability, agent orchestration, model serving, rag, guardrails.
In the past 30 days, Zendesk has posted 17 new AI-related roles. That is a -45% change versus the prior 30 days (31 → 17).
| Title | Stage | AI score |
|---|---|---|
| Staff Machine Learning Engineer Staff Machine Learning Engineer to own the ML surface of routing and presence products, transitioning from a rules-based engine to an agentic routing engine. The role involves end-to-end ML ownership from feature engineering and model design to production serving and monitoring, with a focus on applied ML for measurable customer outcomes at scale within a product engineering team. Responsibilities include designing experimentation frameworks, shaping the integration of classical ML and LLM components, and mentoring other engineers. | AgentServe | 7 |