Job Description
We are on the lookout for aSenior Machine Learning Engineerto join the Tech Foundations - Global Machine Learning Platform team. This role is a premier opportunity to define the architectural future of ML and AI Platforms and drive significant business value across Delivery Hero's diverse brands (Foodora, Foodpanda, Glovo, Talabat, and more).
Your mission is to build and evolve the cutting-edge, centralized platform that empowers Data Science and Machine Learning Engineering teams to rapidly, reliably, and safely develop, deploy, and manage high-impact, personalized ML models for millions of customers every day.
Key Expectations & Responsibilities
1. Architectural Leadership: Define the long-term technical vision, roadmap, and architecture for components of the Global ML Platform. Lead the design, build, and maintenance of scalable ML infrastructure services (, Model Development, Training, Serving, Monitoring) that manage the entire ML lifecycle at scale.
2. System Ownership and Innovation: Drive complex, ambiguous projects end-to-end, translating pain points from cross-functional application teams into robust, high-impact platform features. Proactively champion and implement new technologies and architectures to support novel use cases.
3. Scalability and Resilience: Implement highly available, secure, and performant systems utilizing deep expertise in modern public cloud infrastructure (GCP or equivalent), leveraging Infrastructure as Code (Terraform) and container orchestration (Kubernetes, Helm). Optimize solutions for performance, security, and efficiency on a global scale.
4. Mentorship and Standards: Define and enforce engineering best practices (, GitOps, Software Design) by writing and sharing comprehensive technical documents and RFCs. Act as a technical mentor, guiding and reviewing the work of junior and mid-level engineers to ensure code quality, consistency, and team-wide technical excellence.
Qualifications
5. ML Platform Mastery: At least 5+ years of relevant experience in Software Engineering, Machine Learning Engineering, MLOPs, or Platform Engineering. Proven ability to write high-quality, maintainable code in modern programming languages such as Python or Golang.
6. Architectural Depth: Extensive, hands-on experience designing and building new complex applications and distributed systems from scratch. Proficient or Skilled in applying Software Design, Microservices, and Cloud Architecture Best Practices (GitOps, MLOps, DevOps).
7. Infrastructure Expertise: Demonstrated mastery of containerization and orchestration tools (Docker, Kubernetes, and Helm), CI/CD pipelines, and Infrastructure as Code (Terraform or equivalent).
8. MLOps Ecosystem: Deep familiarity with the entire ML engineering ecosystem, including Model Serving, Training, and Feature Engineering pipelines.
9. ML Savvy: Experience with ML tools like Metaflow, MLflow, Argo Workflows, and Jupyter Notebooks, Serving Frameworks such as Triton
10. Cloud Proficiency: Strong prior knowledge and hands-on experience with public cloud platforms (Google Cloud and AWS).
11. Advanced Problem-Solving: Expert in technical deep-dives, complex investigations, debugging, and Root Cause Analysis (RCA) in large-scale, existing systems. Proven ability to conduct technical explorations and propose solutions based on comprehensive pros/cons analysis.
12. Communication & Influence: Exceptional clarity in written and verbal English communication, with experience contributing to and influencing architectural reviews and cross-team technical strategy.