Senior Machine Learning Product Engineer, Communications, Growth Alliance

HelloFresh is a global food company operating a subscription-based, direct-to-consumer model. HelloTech, its global technology backbone with over 1000 employees, builds the digital platforms supporting various products from meal kits to specialty offerings. The Warsaw office contributes to scalable, data-driven products that enhance customer experiences across markets.

• Hands-on experience with practical AI tooling usage beyond casual experimentation
• Deep expertise in data and ML engineering, including production model operation
• Statistical literacy to design experiments and interpret model metrics
• Ideally over 5 years of experience in production ML systems
• Proficiency with Python, Spark, Databricks, and knowledge of Go, Kafka, Kubernetes
• Ability to diagnose and fix production system issues
• Strong product sense supported by data and user evidence
• Degree (field not specified)

• Own and elevate ML infrastructure, including core repositories, developer tooling, and engineering workflows
• Build and operate data products and machine learning systems from experimentation to production
• Architect Send Decisioning data layer by optimizing communication platform integrations and enhancing feature availability
• Engineer AI-assisted content and asset systems to streamline email copy generation and context-driven asset retrieval
• Build production-grade pipelines and serving layers ensuring observability, reliability, and error handling
• Bridge research and production by partnering with Data Scientists to deploy resilient models
• Set ML engineering standards and maintain core repositories, CI/CD templates, and ML platform practices
• Monitor and improve production systems for reliability, data quality, and latency
• Operate what you build with continuous improvement and monitoring

What you’ll bring: The Ingredients

• Hands-on experience with AI tooling (e.g., Claude Code, Cursor, Copilot)
• Deep data/ML engineering expertise and statistical literacy for experiment design
• Ideally 5+ years building and operating production ML systems
• Fluency in the data and ML stack (Python, Spark, Databricks) and working knowledge of backend and platform stack (Go, Kafka, Kubernetes)
• Operational judgment for diagnosing system issues and deploying fixes
• Product sense backed by data and user evidence