Job Description
We are looking for a (Senior) Machine Learning Engineer to join the Logistics Optimization team on our journey to always deliver amazing experiences.
The Optimization Tribe's purpose is to maximize Delivery Hero's logistics performance. We achieve this by owning and continuously improving the sophisticated algorithm that solves the real-time vehicle routing problem, assigning orders efficiently every minute. This directly impacts DH's core efficiency and customer satisfaction KPIs, influencing decisions for stakeholders from Operations to Product.
As an ML Engineer, your value lies in tackling the problems at scale head-on and building ML products which shape the algorithm's logic and decisions to meet our long-term goals of best-in-class efficiency and service, making a tangible impact across the Delivery Hero network. Are you ready to take your Machine Learning Engineering skills to the next level and make a tangible impact on millions of users worldwide?
In this role you will,
1. Build the infrastructure that powers critical model inputs fed every minute to the dispatch algorithm which shapes experiences for millions of customers across the globe, every single day.
2. Design, build and maintain a scalable ML infrastructure to manage the entire ML lifecycle at scale.
3. Develop Tooling: Build and enhance ML engineering tooling for Model Development, Model Workbench, Model Training, Model monitoring, and Model serving.
4. Collaborate and Optimize: Work closely with data scientists, analysts and data engineers to understand their needs and build solutions that optimize their workflows.
5. Leverage Cloud: Utilize modern technologies and public cloud infrastructure to build highly available systems for multiple teams.
6. Scale and Perform: Build solutions at scale, optimizing for performance and efficiency.
7. Address Pain Points: Understand the pain points of application teams and translate them into robust platform features.
8. Innovate and Suggest: Proactively suggest how the team can leverage new technologies and architectures to support new use cases.
Qualifications
9. 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.
10. Technical Evaluation: Experience in tech explorations to evaluate technologies for business problems and proposing solutions based on comprehensive pros and cons analysis.
11. Proof of Concept: Prior experience building proof of concepts for solving system design challenges using open-source tools.
12. Application Development: Experience building new applications from scratch.
13. System Analysis: Experience in deep-diving into existing systems for investigations and improvements.
14. ML Engineering Experience: Prior experience in ML engineering or MLOps.
15. Communication Skills: Fluency in English. Clarity in communication, both written and verbal.
Nice to Have
16. Prior knowledge of public cloud platforms (AWS, Google Cloud or equivalent).
17. Experience with CI/CD pipelines and Infrastructure as Code (Terraform or equivalent).
18. Familiarity with the ML engineering ecosystem, such as Model serving, training, and feature engineering pipelines.
19. Knowledge of containerization and orchestration tools like Docker, Kubernetes, and Helm.
20. Experience with ML tools like Metaflow, MLflow, Argo Workflows, and Cloud Notebooks.