Machine Learning Engineer

We are the platform turning browsing into shopping. We connect 200 million shoppers with deals they love while boosting local sales for hundreds of top retailers and brands.

We help consumers save time and money while making smart shopping decisions, and we support retailers and brands in engaging customers from online research to in-store purchases.

In 2024, Shopfully joined forces with the North American company Flipp, creating a global leader in the sector. Together, we reach 400 million households and serve over 1,000 top retailers and brands across 27 markets, including Europe, Canada, the USA, Latin America, and Australia.

Ready to spark your growth with us?

WHO WE LOOK FOR 🦄

We are looking for a Senior Machine Learning Engineer to join our Audience Platform team within Ad Products. In this role, you will own and extend both the machine learning systems and the corresponding backend infrastructure that power 1P, 2P, and 3P audience data across Flipp and Shopfully.

This is an engineering-first role for someone who builds and operates production ML systems end-to-end—not a research scientist handing off code to someone else to productionize.

You bring hands-on comfort with statistics, model selection, building, tuning, and experiment design, combined with strong software engineering fundamentals to build scalable pipelines on core tables running into tens of billions of rows.

If you are passionate about MLOps, scalable feature engineering, and shipping production ML systems that drive measurable business impact, this role is for you!

WHAT YOU WILL DO 🏄

• End-to-End ML Systems Ownership: Design, build, deploy, and monitor production ML models and pipelines—such as the IAB Segmentation Model, Retailer & Category Affinity Segments, and Custom Segment Toolkits.  
• Feature & Data Engineering at Scale: Build and maintain high-volume feature engineering and data pipelines using Spark, Databricks, Python, and SQL, working with core tables containing tens of billions of rows.  
• MLOps & Model Lifecycle Management: Implement robust MLOps practices, including model registries, offline/online evaluation, experiment tracking (MLflow or equivalent), and monitoring for data/model drift and quality.  
• Audience Platform Integration: Collaborate on the platform side (Audience API, User Profile API, Kafka Topics, and activation integrations like DV360, Braze, and TTD) to ensure models seamlessly integrate into downstream production services.