Senior Machine Learning Product Engineer, Communications, Growth Alliance (all genders)

Work with HelloFresh in Warsaw and its HelloTech organisation, HelloFresh’s global technology backbone with more than 1000 people, building digital products to power an end-to-end food experience including meal kits, ready-to-eat meals, pet food, and premium meat & seafood. HelloTech creates platforms that serve millions of customers monthly across multiple brands via shared platforms, data, and operational excellence. The role involves shaping scalable, data-driven products with a modern tech stack and international teams.

• Hands-on experience using AI tooling daily (e.g., Claude Code, Cursor, Copilot), with practical knowledge of context shaping AI outputs.
• Deep data/ML engineering expertise with experience operating models or data products in production.
• Ideally 5+ years experience building and maintaining production ML systems.
• Fluency with data and ML stack (Python, Spark, Databricks) and working knowledge of backend and platform stack (Go, Kafka, Kubernetes).
• Statistical literacy to design experiments and accurately evaluate model metrics.
• Operational judgment to diagnose production system issues and deploy robust fixes.
• Full ownership mindset, bias for shipping, and completing work thoroughly.
• Strong product sense with the ability to form opinions on what should be built backed by data and user evidence.

Core initiatives:

• Improve data layer and integration for Send Decisioning engine to optimize communication channels (email, SMS, push).
• Support Intelligent Funnel initiative to personalize the post-click experience and streamline email marketing copy generation.
• Develop AI-assisted content and search systems for automated image selection and marketing asset composition.

Engineering and operational responsibilities:

• Own and enhance ML infrastructure, developer tooling, and engineering workflows for smooth production ML deployments.
• Build and operate data products and machine-learning systems from experimentation to production with real-world performance monitoring.
• Architect and optimize communication platform integrations and feature store for decisioning models.
• Build scalable integrations for AI-assisted content generation and context-driven asset retrieval.
• Deliver production-grade pipelines and serving layers ensuring observability, data reliability, and error handling.
• Collaborate with Data Scientists to transition experimental models into resilient production services.
• Set ML engineering standards including core repositories, CI/CD templates, and ML platform practices (e.g., Databricks, MLflow).
• Take full accountability for production reliability, monitoring input data quality, drift, and latency.
• Operate and continuously improve systems in production beyond specific specialization.

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