Senior ML Engineer

TL;DR

Senior ML Engineer (Retail Media): Building production-grade machine learning systems for a new Retail Media product with an accent on end-to-end ML lifecycle and scalable MLOps. Focus on designing and operating ML pipelines for ranking, segmentation, campaign optimization, attribution, and performance measurement while ensuring reliability, observability, and maintainability in production.

Location: Cologne (Remote possible)

Salary: 78.000 - 98.000 €

Company

hirify.global is Europe’s leading e-pharmacy, building retail media and other data-driven products with modern AI and data capabilities.

What you will do

• Design, develop, and operate production-grade ML systems for retail media use cases (sponsored product ranking, audience segmentation, campaign optimization, attribution, performance measurement).
• Translate business and product requirements into scalable ML solutions balancing model quality, latency, reliability, scalability, and maintainability.
• Build robust ML pipelines for feature engineering, training, evaluation, deployment, monitoring, and continuous model improvement.
• Bring models into production on a cloud-based stack and ensure they are reliable, observable, and maintainable over time.
• Communicate technical decisions, assumptions, limitations, and uncertainty clearly to product, engineering, and business stakeholders.
• Contribute to ML engineering standards, best practices, and knowledge sharing.

Requirements

• Several years of hands-on experience as a Machine Learning Engineer, ML-focused Software Engineer, or Data Scientist with strong engineering background.
• Experience building and operating production-grade ML systems, pipelines, or model-based products.
• Experience with retail media, or alternatively recommender systems or ranking.
• Comfort working with complex data and understanding common ML failure modes (data leakage, feedback loops, distribution shifts, misleading offline metrics).
• Ability to explain complex technical topics clearly to non-technical stakeholders and work closely with business.
• Ownership mindset and comfort navigating ambiguity in an early-stage product environment.

Culture & Benefits

• Remote possible; work-from-home can be arranged individually if office presence is not required (up to 20 days per year anywhere in the EU).
• Support for mental health via anonymous, free psychologist help.
• Personal development through in- and external trainings.
• Deutschland Ticket fully costed for commuting.
• Sports membership support via Urban Sports Club.

Hiring process

• Interviews focused on ML engineering experience, production systems, and collaboration with product/business stakeholders.
• Discussion of technical decisions, trade-offs, and how you handle ML risks and uncertainty.