Job Description
We are on the lookout for a Senior Machine Learning Engineer, Vendor Data to join our AdTech tribe on our journey to always deliver amazing experiences.
As part of our Vendor Team, you’ll be the driving force behind the success of thousands of restaurants, shops, and local businesses. Your contributions will empower vendors with advanced tools to manage their operations, boosting their visibility and reach. Every feature you help build will create growth opportunities for businesses of all sizes, strengthening Delivery Hero’s ecosystem and impact.
1. Design, develop, and deploy machine learning models and services tailored to specific product requirements.
2. Collaborate with cross-functional teams to understand business objectives and translate them into effective ML solutions.
3. Implement scalable and reliable infrastructure to support machine learning services in a production environment.
4. Conduct performance analysis and optimization of ML services to ensure efficiency and effectiveness.
5. Provide technical expertise and support to stakeholders during the integration and deployment of machine learning solutions.
6. Collaborate with data scientists, ML engineers, software engineers, and other stakeholders to ensure seamless integration of ML services into existing systems.
Qualifications
7. Proven experience in developing and deploying machine learning models in a production environment.
8. Deep understanding of storage and caching methodologies.
9. Strong programming skills in languages such as Python, Java, or Golang.
10. Experience with machine learning frameworks and libraries (, TensorFlow, PyTorch).
11. Solid understanding of cloud platforms (, AWS, Azure, GCP) and containerization technologies (, Docker, Kubernetes).
12. Excellent interpersonal skills and the ability to collaborate in interdisciplinary teams and stakeholders including product and engineering teams.
Nice to have:
13. Experience working in an Ad Tech data science team involving neural network-based machine learning solutions to ranking, recommendations, and bidding.