Senior Machine Learning Product Engineer, Risk and Fraud Management, Payments, Growth Alliance (all genders)Active

HelloFresh
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The opportunity

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

  • Architect and operate real-time and streaming production ML systems: end-to-end, maintaining high availability, tight latency targets, and dependable fallback mechanisms.

  • Build and optimize data pipelines and inference workflows using PySpark on: Databricks, Kafka, and modern orchestrators (Airflow/Prefect) to support checkout scoring and automated payment recovery.

  • Drive key platform migrations, transitioning feature stores and model serving: to Databricks to guarantee online/offline consistency and point-in-time correctness.

  • Lead the technical delivery of our Smart Payment Retry engine, establishing: the experimentation framework and rolling out ML-driven charge re-attempt policies across international markets.

  • Establish modern MLOps practices, including CI/CD automation, model registry: versioning, automated validation gates, and infrastructure as code.

  • Implement automated monitoring and experimentation plumbing—from drift: detection to shadow deployments and kill switches—to catch regressions before they reach customers.

What they're looking for

  • Collaborate cross-functionally with data scientists to pressure-test models: for production readiness while mentoring teammates on system design and AI-augmented engineering workflows.
  • Work beyond your specialization when the problem demands it. Your: specialization is your anchor, not your boundary.
  • Operate what you build. You instrument, monitor, and improve your systems in: production. Shipping is the beginning of the learning cycle, not the end.
  • Hands-on experience working with AI tooling (e.g., Claude Code, Cursor,: Copilot) beyond casual experimentation. You use AI agents every day. You have a practical sense of how the context you provide to AI tools shapes output quality, and how to set boundaries on AI-generated work.