Staff Backend Engineer, Customer Value Optimization

About the role

The Staff Backend Engineer in Customer Value Optimization will own the design and implementation of production-grade infrastructure that powers machine learning models at scale within HelloFresh. You will serve as the critical technical bridge that transforms experimental data science outputs into reliable, high-performance backend systems. This position demands deep ownership of the end-to-end lifecycle of model serving, from initial integration through long-term maintenance and optimization. You will define and drive architectural standards that ensure consistency, security, and scalability across multiple engineering squads. The role requires close collaboration with Data Scientists to translate analytical requirements into robust software specifications and deployment pipelines. You will lead cross-functional technical initiatives, providing guidance and hands-on implementation for complex problems that impact customer value. Furthermore, you will mentor engineers throughout the organization, elevating the quality of code and system design across the tribe. Ultimately, your work will set the technical direction that safeguards model integrity and enables data-driven decision-making at every level.

Key facts

What you'll do

• Architect and maintain the scalable infrastructure responsible for deploying and serving machine learning models in production environments at HelloFresh.
• Build and optimize data pipelines that ensure the reliable delivery, low-latency execution, and high availability of model inference workloads.
• Define and enforce organization-wide standards for API design, system architecture, and model serving protocols to ensure interoperability and quality.
• Establish technical direction and elevate implementation quality by setting benchmarks for performance, reliability, and scalability across engineering squads.
• Partner intimately with Data Scientists to translate analytical models into production-ready backend solutions that meet strict performance and observability criteria.
• Ensure that all deployed models maintain rigorous standards for observability, monitoring, and testing to guarantee consistent and predictable behavior.
• Lead cross-squad technical initiatives, guiding complex architecture decisions and contributing directly to the implementation of critical systems.
• Mentor engineers across multiple squads, sharing expertise and best practices to improve the overall quality and robustness of backend development.
• Contribute to tribe-wide engineering standards and help shape the technical roadmap for customer value optimization initiatives.
• Safeguard model behavior and business logic through the implementation of comprehensive testing strategies, monitoring frameworks, and reliability practices.
• Support Data Science colleagues by providing the backend foundation necessary to achieve customer value goals and overarching business objectives.
• Drive the evolution of MLOps practices, ensuring that model deployment, versioning, and monitoring are efficient and scalable.
• Collaborate with Product and Data teams to align backend infrastructure with evolving business requirements and strategic priorities.
• Act as a thought leader in backend and ML infrastructure, researching and proposing innovative solutions to complex technical challenges.

Requirements

• Possess 7 or more years of professional backend engineering experience, with a significant portion focused on high-scale systems.
• Have Python as a core component of your technical foundation, demonstrating advanced proficiency in the language and its ecosystem.
• Show a proven track record of building, operating, and maintaining ML model serving systems or ML platforms within production environments.
• Exhibit deep knowledge of distributed systems principles, microservice architecture patterns, and the design of robust API contracts.
• Demonstrate the ability to work effectively across team boundaries, managing complex, cross-functional projects with minimal supervision.
• Communicate clearly and effectively with diverse stakeholders, including Data Scientists, Machine Learning Engineers, and Data Engineers.
• Embrace a collaborative, low-ego mindset, combining strong technical judgment with a willingness to learn from others.
• Understand the fundamentals of data pipelines, feature engineering, and the challenges inherent in productionizing machine learning.
• Be comfortable making technical decisions that balance innovation with the need for stability, reliability, and maintainability.

Nice to have

• Hands-on experience with ML frameworks and serving tools such as TensorFlow Serving, TorchServe, or similar platforms.
• Practical familiarity with feature stores, model registries, and broader MLOps tooling and workflows.
• Prior work in e-commerce, subscription-based businesses, or personalization domains, providing context for customer-centric metrics.

Practical notes

Please ensure that your application reflects the requirements and responsibilities outlined in this description. This document serves as a summary of the role and does not replace official application materials.