Braze

This role focuses on designing and building end-to-end machine learning solutions, specifically RL use cases, for customer engagement personalization. The individual will own the full ML pipeline from data transformation to model training and activation, drive customer success through technical guidance, and contribute to extending product capabilities and shaping AI product strategy. The role involves partnering with the product team to advance reinforcement learning algorithms and bring customer-facing insights to the product roadmap.

This role focuses on designing and building end-to-end machine learning solutions, specifically RL use cases, for customer engagement personalization. The individual will own the full ML pipeline from data transformation to model training and activation, drive customer success through technical guidance, and contribute to extending product capabilities and shaping AI product strategy. The role involves partnering with the product team to advance reinforcement learning algorithms and bring customer-facing insights to the product roadmap.

Senior Software Engineer to design, improve, and scale Braze's self-learning (reinforcement learning) AI platform for marketing personalization. The role involves building modular components for Decisioning Studio, working with cross-functional teams, and influencing product strategy.

The Forward Deployed Data Scientist, AI Deployment role at Braze focuses on partnering with customers to implement and ensure success with BrazeAI. This involves defining use cases, integrating data, setting up pipelines, configuring ML models, and improving architecture. The role also involves refining reinforcement learning algorithms, contributing to product strategy through customer insights, and providing ongoing technical expertise for customer adoption and success. The position requires strong Python, ML libraries, SQL, and ML pipeline experience, along with engineering best practices.