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

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 and premium meat & seafood, HelloTech creates the platforms that bring tailored food solutions to millions of customers every month. Our subscription-based, direct-to-consumer model relies on technology at every step, from customer-facing apps and personalization logic to pricing, forecasting, supply chain optimization, and initiatives that help reduce food waste. While our brands operate independently to serve distinct customer needs, they are united by shared platforms, data, and operational excellence built by HelloTech. HelloTech works in autonomous, cross-functional alliances, each owning a specific product or domain end to end. By working with our Warsaw office, you will help shape scalable, data-driven products used across our markets, working with a modern tech stack and international teams to continuously improve how people discover, order, and enjoy HelloFresh’s products, today and in the future. About the role: What's in the Box As a Senior Machine Learning Product Engineer in our Risk and Fraud Management squad, you will own the production machine learning systems that protect HelloFresh from checkout fraud and intelligently recover failed recurring payments across our global markets. You will take predictive models from initial concept to high-availability, low-latency scoring systems on Databricks, directly balancing risk mitigation against a frictionless customer experience. In our AI-first engineering environment, you will drive core architectural milestones - such as our Smart Payment Retry engine - while leveraging AI coding agents to accelerate development, deployment, and testing. Ultimately, your work will protect revenue and ensure reliable, fair decisioning for millions of active customers. At HelloTech, flexibility and cross-functional collaboration are core to how we work. While this role is aligned to a specific Alliance, strong candidates may also be considered for opportunities across different teams or projects. What you’ll do: The Recipe At HelloFresh we are moving away from a model where software developers just execute tickets toward one where product engineers are trusted to own customer problems. A Product Engineer takes a problem, forms a point of view, validates it with customers and data, and ships it using AI as a force multiplier. • 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. • 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. What you’ll bring: The Ingredients • 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. • Production ML track record: A solid background (5+ years) taking machine learning systems from prototype into high-traffic, low-latency cloud environments. • Strong distributed data foundation: Deep command of Python, SQL, and PySpark on Databricks, paired with hands-on containerized and serverless model deployment. • Cloud & MLOps proficiency: Hands-on familiarity with cloud environments (AWS), containerization, and modern orchestration tools (such as Databricks Model Serving, Airflow, Kafka, or Terraform) to maintain event-driven pipelines. Proven capability building automated CI/CD pipelines. • Pragmatic product mindset: Sound literacy in model evaluation (precision/recall balance, drift, A/B testing) combined with a bias for shipping and clear communication across disciplines. • Deep data/ML engineering expertise, experience operating models or data products in production, and the statistical literacy to design sound experiments and interpret their results. • You take full ownership. You have a bias to ship. You finish the last twenty percent. • You have product sense and are opinionated about what should be built and why, and you can back that opinion with data and user evidence. Above all, we are looking for individuals who will make HelloFresh better. We believe there are many different ways of developing skills and we love diverse experiences! So even if you don’t “tick all the boxes” but think you’d thrive in this role, we would really like to learn more about you. What we offer: The Toppings • Global collaboration at sc