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Machine Learning Engineer ( Consumer - Global Recommendations & AI - Machine Learning Platform ), Berlin
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Client:
Delivery Hero
Location:
Berlin, Germany
Job Category:
-
EU work permit required:
Yes
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Job Reference:
65e0f4812e91
Job Views:
1
Posted:
27.04.2025
Expiry Date:
11.06.2025
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Job Description:
Job Description
We are on the lookout for a Machine Learning Engineer to join the Consumer - Global Recommendations & AI - Machine Learning Platform tribe on our journey to always deliver amazing experiences.
At Delivery Hero, you’ll shape the global consumer experience for millions of users. Your work will have a direct impact on how we attract and retain customers, creating data-driven, personalized interactions that keep them coming back. As part of our Consumer Team, you’ll enhance customer satisfaction and drive global growth and profitability through innovative projects.
Are you ready to take your Machine Learning Engineering skills to the next level and make a tangible impact on millions of users worldwide? We're seeking a talented Machine Learning Engineer to join our innovative Global Machine Learning Platform team. Our mission is to empower Data Science teams across Delivery Hero's diverse brands (Foodora, Foodpanda, Glovo, Talabat, Peya, Hungerstation, Woowa, and more) with a cutting-edge, scalable platform. This platform enables them to rapidly and reliably develop, deploy, and manage machine learning models that drive personalized experiences for our global customer base.
In this role you will,
Build the infrastructure that powers personalized experiences for millions of customers across the globe, every single day.
Design, build and maintain a scalable ML infrastructure to manage the entire ML lifecycle at scale.
Develop Tooling: Build and enhance ML engineering tooling for Model Development, Model Workbench, Model Training, Model monitoring, and Model serving.
Collaborate and Optimize: Work closely with data scientists and data engineers to understand their needs and build solutions that optimize their workflows.
Leverage Cloud: Utilize modern technologies and public cloud infrastructure to build highly available systems for multiple teams.
Scale and Perform: Build solutions at scale, optimizing for performance and efficiency.
Address Pain Points: Understand the pain points of application teams and translate them into robust platform features.
Innovate and Suggest: Proactively suggest how the team can leverage new technologies and architectures to support new use cases.
Qualifications
Software Engineering Expertise: At least 4 years of experience in software engineering with hands-on experience with modern programming languages such as Python, Java, or Golang.
Technical Evaluation: Experience in tech explorations to evaluate technologies for business problems and proposing solutions based on comprehensive pros and cons analysis.
Proof of Concept: Prior experience building proof of concepts for solving system design challenges using open-source tools.
Application Development: Experience building new applications from scratch.
System Analysis: Experience in deep-diving into existing systems for investigations and improvements.
ML Engineering Experience: Prior experience in ML engineering or MLOps.
Communication Skills: Fluency in English. Clarity in communication, both written and verbal.
Nice to Have
Prior knowledge of public cloud platforms (Google Cloud or equivalent).
Experience with CI/CD pipelines and Infrastructure as Code (Terraform or equivalent).
Familiarity with the ML engineering ecosystem, such as Model serving, training, and feature engineering pipelines.
Knowledge of containerization and orchestration tools like Docker, Kubernetes, and Helm.
Experience with ML tools like Metaflow, MLflow, Argo Workflows, and Cloud Notebooks.
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