Braze
Enterprise · Marketing automation
Currently tracking 15 active AI roles, down 25% versus the prior 4 weeks. Primary focus: Post-train · Engineering. Salary range $98k–$165k (avg $138k).
Braze currently has 26 active job listings related to AI. The majority of these roles are focused on the application stage, representing 42% of the openings, followed by post-training roles at 38%. Engineering is the most active function, with 15 listings, and the United States is the primary hiring country with 10 positions. The company is frequently seeking candidates with experience in model serving, fine-tuning, and recommender systems. In the last 30 days, Braze has posted 5 new AI roles, a 150% increase compared to the previous 30-day period.
Enterprise · Marketing automation
Braze currently has 23 active AI-related roles in our index. The most common open titles are: Engagement Manager II (8), Forward-Deployed Data Scientist II (7), Forward Deployed Data Scientist, AI Deployment, Data Scientist, AI Deployment, Forward-Deployed Data Scientist. Most positions are in Engineering and Product.
Braze's active AI hiring is concentrated in: application (43%), post-training (39%), agents (13%). 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 (10 roles), Brazil (4 roles), Canada (3 roles), Romania (2 roles).
Job postings at Braze most frequently reference: model serving, fine tuning, rl post training, recommender systems, agent orchestration.
In the past 30 days, Braze has posted 3 new AI-related roles. That is a -50% change versus the prior 30 days (6 → 3).
| Title | Stage | AI score |
|---|---|---|
| Forward-Deployed Data Scientist II The Forward-Deployed Data Scientist II at Braze designs and builds end-to-end machine learning solutions for customer personalization. This role involves scoping ML use cases, building and owning the full ML pipeline from data transformation to model training and activation, driving customer success through technical guidance, extending product capabilities, and partnering with the Product team to advance reinforcement learning algorithms and shape AI product strategy. | Post-trainAgent | 7 |
| Senior Data Scientist (AI Deployment) Senior Data Scientist role focused on deploying and refining AI models, particularly reinforcement learning algorithms, for customer engagement within the Braze platform. This involves collaborating with customers, improving ML pipelines, and contributing to product strategy. | Post-trainData | 7 |
| Forward-Deployed Data Scientist II The Forward-Deployed Data Scientist II at Braze partners with customers to ensure their success with BrazeAI. This role involves collaborating on implementations, improving architecture, developing data pipelines and components, refining reinforcement learning algorithms, and contributing to product strategy. The position requires strong Python and ML library proficiency, SQL skills, and experience with ML pipelines and model deployment, along with engineering best practices. | Post-trainData | 7 |
| Forward-Deployed Data Scientist II The Forward-Deployed Data Scientist II at Braze partners with customers to ensure their success with BrazeAI. This role involves collaborating on implementations, improving architecture, developing data pipelines and components, refining reinforcement learning algorithms, and contributing to product strategy. The position requires strong Python and ML library proficiency, SQL skills, and experience with ML pipelines and model deployment, along with engineering best practices. | Post-trainData | 7 |
| Forward-Deployed Data Scientist II The Forward-Deployed Data Scientist II at Braze partners with customers to ensure their success with BrazeAI. This role involves collaborating on implementations, improving architecture, developing data pipelines and components, refining reinforcement learning algorithms, and contributing to product strategy. The position requires strong Python and ML library proficiency, SQL skills, and experience with ML pipelines and model deployment, along with engineering best practices. | Post-trainData | 7 |
| Forward-Deployed Data Scientist II The Forward-Deployed Data Scientist II at Braze partners with customers to ensure their success with BrazeAI. This role involves collaborating on implementations, improving architecture, developing data pipelines and components, refining reinforcement learning algorithms, and contributing to product strategy. The position requires strong Python and ML library proficiency, SQL skills, and experience with ML pipelines and model deployment, along with engineering best practices. | Post-trainData | 7 |
| Data Scientist, AI Deployment Data Scientist, AI Deployment at Braze, responsible for designing and building end-to-end ML solutions for customer personalization. This role involves owning the full ML pipeline from data transformation and model training to activation, and extending product capabilities. They will also partner with the Product team to advance reinforcement learning algorithms and shape AI product strategy. | Post-trainServe | 7 |
| Forward-Deployed Data Scientist II The Forward-Deployed Data Scientist II at Braze partners with customers to ensure their success with BrazeAI. This role involves collaborating on implementations, extending product capabilities by improving architecture and developing data pipelines, refining reinforcement learning algorithms, contributing to product strategy, and providing technical expertise for customer adoption and success. The role requires strong Python, ML libraries, SQL, and ML pipeline/deployment experience, with a preference for customer-facing roles and experience with RL algorithms. | Post-trainData | 7 |
| Forward-Deployed Data Scientist The Forward-Deployed Data Scientist at Braze partners with customers to implement BrazeAI solutions, focusing on ML model configuration, data integration, and refining reinforcement learning algorithms. This role extends product capabilities by developing reusable data pipelines and components, and contributes to product strategy through customer insights. | Post-trainData | 7 |