Senior Machine Learning Product Engineer, Communications, Growth AllianceActive

Work with HelloFresh in Warsaw and its HelloTech organisation, HelloFresh’s global technology backbone with more than 1000 people, building the digital products that power our end-to-end food experience. From meal kits and ready-to-eat meals to specialty offerings like pet food…

What you'll do

• Send Decisioning: Improving the data layer and integration for the decisioning engine, specifically utilizing the company's existing feature store and feature sets, to optimize our communication channels (email, SMS, and push notifications). Your models will automate the logic that determines the most relevant message and timing for each individual user.

• Intelligent Funnel: Supporting a collaborative, cross-tribe initiative aimed at personalizing the post-click experience. You will streamline the content generation process to reduce manual work when creating email marketing copy.

• AI-Assisted Content & Search: Developing automated asset retrieval to select images based on context during email creation, moving away from manual selection. Additionally, you will develop tools that assist marketing teams in the automated composition of new, high-performing communication assets.

• Own & elevate ML infrastructure: Build, maintain, and refine the core repositories, developer tooling, and engineering workflows that enable the team to organize, scale, and deploy production ML systems smoothly.

• Build and operate data products and machine-learning systems , taking them: from experimentation through production and owning their real-world performance.

• Architect the Send Decisioning data layer: Evaluate, optimize, and potentially redesign our communication platform integrations - building robust pipelines and integrating our feature store to enhance data quality and feature availability for decisioning models.

What they're looking for

• Engineer AI-assisted content & asset systems: Develop scalable integrations and workflows to streamline email copy generation (subject lines, copy context) and build context-driven asset retrieval systems for marketing teams.
• Build production-grade pipelines & serving layers: Translate research and experimentation into low-latency, highly reliable production systems, ensuring continuous observability, data reliability, and error handling.
• Bridge research & production: Partner closely with Data Scientists to transition experimental models into resilient, low-latency production services with high observability and robust fallback mechanisms.
• Set the ML engineering standard: Build, standardize, and maintain core repositories, CI/CD templates, and ML platform practices (Databricks, MLflow) to streamline how the team develops and deploys code. Take full accountability for production reliability—monitoring input data quality, drift, and latency across critical communication and personalization funnels.