Senior Applied AI and Machine Learning Engineer

About Raise

Loyalty Raise is developing an AI-native loyalty platform that helps brands plan, run and measure promotions for first purchases, repeat purchases and reactivation. Brands set goals and budgets, review AI recommendations and approve campaigns. We are making Raise our first customer, with external design partners using the same core in their own customer experiences.

This new product builds on Raise's established commerce, payments and gift card business. Our longer-term roadmap includes a rewards API for personal assistants and commerce agents. We are a small, hands-on product team within Raise, with direct access to product and engineering leadership.

The role

Own both the conversational AI workflow and the decision models behind measurable loyalty campaigns. Turn business goals into approved campaigns, and develop models that recommend audiences, offers and reward amounts. This individual contributor role combines LLM product engineering, applied machine learning, causal inference and experimentation, working directly with the CTO.

Architecture and data

• Infrastructure. ML runs in Raise's AWS account.
• Cross-program learning. Learning across partner programs is limited to consented aggregate effects. Partner data access is scoped and approved.
• Cost simulation. The role integrates rules-engine cost simulations with explicit response assumptions, and keeps these distinct from model estimates of incremental lift. Campaigns are validated against existing services, with retries and audit trails.
• Learning-data requirements. Measurement depends on randomized holdouts and complete records for non-buyers and no-offer customers, plus outcomes net of reward costs and returns, using verified calculations.
• Partner-specific models. Partner-specific recommendations improve as reliable data accumulates; transparent rules come first where evidence is limited. Monitoring includes tenant isolation. [To add before sharing: current build status, and known data limitations.]

Ownership and team boundaries

You own AI workflows, decision models and their evaluation. Data engineering builds reliable pipelines, features and measurement datasets to jointly defined requirements. The CTO and backend engineers own rules, execution and reward accounting.

Broader analyst features, catalog search, embeddings and fine-tuning are evaluated as needed and are not separate first-release commitments.