Senior Machine Learning Product Engineer, Communications, Growth Alliance

The Communications tribe, a key part of the Growth Alliance, enables our brands to deliver highly personalized, meaningful interactions with our users across global markets. As a Senior Machine Learning Product Engineer within this team, you will drive a direct business impact by shaping the intelligence behind how, when, and what we communicate with our customers.

Your work will primarily focus on three core initiatives:

• 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.

To succeed in this project, you should be a pragmatic and curious problem solver who excels in a cross-functional environment. Success involves translating complex user behavior into scalable models, identifying opportunities to improve our data foundations, and balancing technical depth with a strong focus on driving user engagement.

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.

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.

• 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.
• 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.
• 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.