AI/ML Engineer

Project Overview:

Data/ML engineer embedded on a product team building customer intelligence and personalization platform for restaurants. You'll own the data layer — from raw transaction data to production ML models — powering merchant-facing features like customer segmentation, behavior insights, diner profiles, and personalized communications.


Responsibilities:
- Build and maintain customer data pipelines that unify transaction, reservation, loyalty, and marketing data into a clean, queryable layer             
- Develop and operate the Customer Personalization Engine in Snowflake Cortex or similar technology — trend analysis, behavioral segmentation, and recommendation models                                                          
- Build the analytical foundation for Actionable Customer Behavior Insights — surface patterns from transaction history that translate into merchant-facing recommendations                                                                           
- Support Global Diner Unification by designing cross-merchant customer identity resolution approaches
- Partner with full-stack engineers to define APIs and data contracts that power merchant-facing features (Pre-Shift Report, Diner Profiles, Segmentation Tool)    
- Own data quality, observability, and pipeline reliability for customer data products                                        
- Advise the team on what's feasible with available data vs. what requires new collection — be the person who says "we have this" or "we need to build that pipe first"   

Competencies

- Snowflake or other datawarehousing experience is required — warehousing, transformations, UDFs, and working within Snowflake's or similar ecosystem day-to-day
- Hands-on experience with Snowflake Cortex or comparable ML-in-warehouse tooling (Databricks ML, BigQuery ML)
- Experience with dbt or equivalent transformation frameworks — building, testing, and maintaining modular data models that serve analytics and ML features
- Familiar with feature engineering for ML — transforming raw transactional data into meaningful signals (recency, frequency, monetary value, behavioral patterns)
- Built production ML systems — not just notebooks. Has shipped models that other teams and products depend on.
- Comfortable with data modeling for messy, multi-source customer identity problems (POS + reservations + loyalty + marketing = one customer)
- Can work embedded on a product team, not just a data team — comfortable pairing with frontend/backend engineers, scoping what's realistic in a sprint, and shipping incrementally
- Familiar with privacy considerations around customer data aggregation and cross-merchant data sharing
- Strong communicator — can explain data constraints and tradeoffs to PMs and engineers without data backgrounds

Nice to Have

- Experience in restaurant/hospitality tech or POS systems
- Experience with real-time or near-real-time feature serving (not just batch)