Senior Applied AI and Machine Learning Engineer

About Raise Loyalty
Raise is building an AI-native loyalty platform that helps brands acquire customers, increase repeat purchases and bring inactive customers back. AI helps teams plan campaigns, recommend offers and measure results, with people setting budgets and approving campaigns.
We’re building and testing the product within Raise, drawing on our established commerce, payments and gift card business. You’ll join a small, hands-on team with direct access to the CTO and product leadership.
The role
Own both the conversational AI workflows and the decision models behind measurable loyalty campaigns. You’ll turn business goals into campaign recommendations and develop models that help determine which customers to reach, what to offer and when.
This role combines production LLM engineering, applied machine learning, causal inference and experimentation. Your work will directly shape the product and how we evaluate its impact.
What you’ll do
Build conversational AI workflows that help users define goals, provide missing information and review campaign recommendations.
Develop and evaluate models for customer behavior, targeting and offer recommendations.
Help design experiments that distinguish likely customer behavior from the incremental effect of an offer.
Define data requirements and success measures with engineering, product and finance.
Build evaluations and monitoring for accuracy, reliability, cost and measurable business outcomes.
Work with data and backend engineers to bring AI capabilities into production with appropriate permissions, privacy controls and human approval.
Choose practical methods for the available data and improve them as evidence accumulates.
What you bring
Experience shipping and operating customer-facing software, typically four or more years, including an LLM application beyond a prototype.
Practical experience developing, evaluating and deploying predictive or decision models.
Experience with LLM APIs, structured outputs, tool calling and retrieval, supported by strong backend and API integration skills.
Applied knowledge of experimental design, randomized tests and treatment-effect estimation or uplift modeling.
An understanding of data leakage, uncertainty, model limitations and production monitoring.
The ability to work independently, explain technical trade-offs and turn an ambiguous problem into a useful product.
A PhD in machine learning or a related quantitative field is preferred. Strong shipped LLM product experience can outweigh the degree preference; demonstrated applied ML capability and production delivery are essential.
Experience in commerce, recommendations, loyalty or lifecycle marketing is helpful.
How we work
You’ll work directly with the CTO and collaborate closely with product, data and backend engineering. This is a hands-on individual contributor role with ownership of AI workflows, decision models and their evaluation.
We’re open to full-time employment or a contract engagement.
Apply
Share your résumé and examples of AI or ML products you’ve shipped. Tell us what you owned, how you evaluated the results and what you learned.

Work arrangement
Yes