Senior Machine Learning Engineer - (Vendor Data)

Overview

Be our next Hero! - Join the world’s leading local delivery platform. We are looking for a Senior Machine Learning Engineer - Vendor Data (all genders) to join the Vendor Data tribe in our mission to deliver consistently amazing experiences. As part of our Vendor Team, you’ll drive the success of thousands of restaurants, shops, and local businesses, empowering vendors with advanced tools to manage their operations and boost visibility and reach.

The Vendor Data tribe’s mission is to drive innovation, insight-driven decision making and to solve business problems through a culture of excellence and openness. The tribe comprises teams of analysts, engineers, and scientists delivering data solutions across Delivery Hero’s 70+ markets.

You will work with a team of ML Engineers and Data Scientists deploying advanced ML services to solve complex problems and drive revenue for Delivery Hero. If you’re a builder in machine learning hungry for a new adventure, an international workplace awaits in Berlin.

Responsibilities

• Drive the design, build and maintain large-scale machine learning systems aligned with Vendor strategic product goals, optimizing for performance and efficiency
• Architect scalable, reliable, and cost-efficient ML infrastructure to manage the ML lifecycle at scale
• Set and uphold best practices for productionising ML, including monitoring, testing, reproducibility, and responsible AI
• Collaborate with product, engineering, analysts, and data science teams to understand needs and build solutions that optimise workflows
• Leverage cloud technologies to build reliable and robust systems for multiple teams; suggest how to leverage new technologies for downstream use cases
• Raise the technical bar and foster a culture of excellence in ML engineering
• Stay ahead of industry and academic trends, evaluating and adopting emerging technologies for Delivery Hero’s ML capabilities

Qualifications

• Expert knowledge of data management, storage, and MLOps/DataOps practices to enable performant, cost-efficient ML systems at scale
• Deep expertise with modern ML frameworks (e.g., TensorFlow, PyTorch) and ability to guide best practices across use cases
• Experience designing, deploying, and operating large-scale ML systems in production with a track record of high-impact solutions
• Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerisation/orchestration (Docker, Kubernetes)
• Experience with CI/CD and ML tools (Metaflow, MLflow, Airflow, Grafana, etc.)
• Strong systems-level problem-solving skills balancing performance, scalability, maintainability, and business impact
• Excellent communication and collaboration skills with cross-functional stakeholders

Highly desirable

• Proven ability to design and scale ML systems under latency, throughput, and cost constraints
• Experience translating research into production systems driving measurable business outcomes

What’s in it for you?

• Hybrid working model with in-person collaboration at our Berlin campus two days a week
• 27 days holiday plus extra days at 2nd and 3rd year of service
• Educational budget (1,000 €), language courses, parental support, Udemy Business access
• Health and wellness perks (health checkups, meditation, gym and bicycle subsidies)
• Employee benefits (share plan, sabbatical bank, transit discounts, life and accident insurance, pension plan)
• Food perks (digital meal vouchers, food vouchers, corporate discounts)

Ready to join our team? If you’re excited to grow, collaborate and be part of the world’s leading delivery platform, apply today!

We believe diversity and inclusion are key to a great product and experience. We do not discriminate on race, religion, color, national origin, gender identities, sexual orientation, age, marital or disability status, or any other characteristic. If you need accommodations for the interview, please include it in your application. You’re welcome to share your pronouns (he/she/they) from the start so we can address you respectfully.

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