S'more about the team

The Growth Tech Alliance's goal is to enable HelloFresh to drive smarter marketing through AI-powered automation, intelligent systems, and advanced optimization. We've pioneered cutting-edge approaches to programmatic campaign optimization using state-of-the-art AI/ML technologies and are now scaling these capabilities across our global marketing operations. Our team’s key responsibilities include designing, building, maintaining autonomous decision-making systems, advanced ML models for marketing optimization, and real-time intelligence that drives measurable business impact. We work at the intersection of modern AI architectures, agentic systems, and enterprise-scale ML operations.

We are looking for a Staff Machine Learning & AI Engineer to lead the engineering architecture of our multi-agent AI systems, with a focus on agent-driven products. In this role, you will bridge the gap between our data scientists and backend engineers, connecting internal automation APIs, AI agents, and the supporting ML models into a cohesive production platform.

At HelloFresh, Staff Engineers are hands-on builders. You will spend roughly 50% of your time writing production code and building critical systems, and 50% driving technical direction through architecture reviews, RFCs, and mentoring engineers and data scientists across the team.

Lettuce share what this role will be responsible for

Design, build, and maintain production multi-agent AI systems and LLM workflows using modern orchestration frameworks such as LangChain and LangGraph.
Build the APIs, tool-calling interfaces, and MCP integrations that connect our internal marketing automation services with AI agents and third-party platforms.
Partner with Data Scientists to take machine learning and statistical models from prototype into reliable production, and connect them to the agents that create, launch, and optimize ads.
Design end-to-end system architectures across our models, databases, and backend services, optimizing for reliability, correctness, and long-running workflows at scale.
Build evaluation pipelines, guardrails, state management, and human-in-the-loop approval workflows to ensure AI and ML systems run safely in production.
Author RFCs and Architecture Decision Records (ADRs) to align the team on technical direction and solve cross-team architectural challenges.
Raise the engineering bar across the team through code reviews, system design guidance, and active mentorship of Senior Engineers and Data Scientists.
All other duties, as assigned

Sound a-peeling? Here's what we're looking for

Extensive hands-on experience in Machine Learning Engineering, AI Engineering, or Backend Software Engineering, with a strong track record of building and operating distributed production systems.
Production proficiency in Python and SQL, with strong fundamentals in software design, API development (e.g., FastAPI), testing, and database optimization.
Hands-on experience building and deploying production LLM and agentic applications beyond simple prototypes, including agent orchestration (LangGraph/LangChain or similar), tool use, MCP, context management, and automated evaluations.
Experience with A/B testing or other experimentation methods (e.g., synthetic testing) to validate changes before and after launch.
Experience with multi-agent systems (e.g., supervisor and sub-agent designs) and the ability to explain the trade-offs between single-agent and multi-agent approaches.
Experience with multimodal AI models (vision-language models for image or video analysis and generation).
Experience building systems where predictive ML models and LLM-based agents work together to drive automated decisions.
Prior background in AdTech, MarTech, e-commerce growth, or high-scale consumer platforms.
Familiarity with deploying and scaling classical machine learning or deep learning models in production, alongside solid MLOps practices (CI/CD, model monitoring, and automated pipelines).
Experience working with modern cloud and data platforms such as Databricks, Unity Catalog, MLflow, Docker, and AWS.