Braze currently has 24 active AI-related job listings. The majority of these roles, 71%, are focused on agents, with application roles making up another 17%. Engineering is the most frequent function for these positions, followed by Product. Hiring is primarily concentrated in the United States, with additional roles in Brazil and the United Kingdom. Frequent tech tags include agent_orchestration, tool_use, and rag, suggesting a focus on developing and deploying AI agents that can interact with external tools and information.
Currently tracking 15 active AI roles, up 10% versus the prior 4 weeks. Primary focus: Post-train · Engineering. Salary range $98k–$165k (avg $138k).
Braze currently has 33 active AI-related roles in our index. The most common open titles are: Director, CX Business Systems (5), Applied AI Architect (4), Senior Lead Systems Engineer, AI & Automation (4), Senior Technical Product Manager, Content Platforms (Localization) (4), Forward Deployed AI Accelerator (3). Most positions are in Engineering and Product.
Braze's active AI hiring is concentrated in: agents (76%), application (18%), post-training (6%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Braze is hiring AI talent in: United States (24 roles), Brazil (4 roles), Japan (2 roles), Canada (2 roles).
Job postings at Braze most frequently mention: Reinforcement Learning (RL), Technical Leadership, System Design, Streaming Data, Robotics.
In the past 30 days, Braze has posted 17 new AI-related roles. That is a +31% change versus the prior 30 days (13 → 17).
| Title | Stage | AI score |
|---|---|---|
| AI Decisioning Technical Lead This role is a hybrid technical lead for AI Decisioning at Braze, focusing on the end-to-end AI lifecycle for Japanese customers. It involves both pre-sales (technical SME, pitching, discovery) and post-sales (AI Success Manager, experimentation design, performance analysis, troubleshooting, data pipeline integration). The goal is to help brands predict customer behavior, automate orchestration, and scale hyper-personalized engagement. The role requires strong SQL, Python/R, ML concepts, MarTech knowledge, and consultative skills, with potential to grow into management. | Ship | 5 |