Senior Machine Learning Engineer - (Logistics, Optimization)

Job description

We are on the lookout 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 second across the world.

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 models and 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 and Data Science skills to the next level and make a tangible impact on millions of users worldwide? In this role you will, Build and Maintain: Design, build and maintain scalable ML models services, optimizing for performance and efficiency.

Design Robust Architecture

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.

Collaborate and Optimize

Work closely with data scientists and engineers to understand their product needs and build scalable solutions.

Develop Tooling

Build and enhance ML engineering tooling for Model Development, Monitoring, Serving, and Experimentation.

Leverage Cloud

Utilize DH cloud infrastructure built with modern technologies on popular cloud platforms to build highly available systems for multiple teams.

Innovate and Suggest

Proactively suggest how the team can leverage new technologies and architectures to support new use cases.

Qualifications

ML Engineering Experience

Prior experience (Ideally 4+ years) in ML engineering or MLOps and familiar with the ML engineering ecosystem tools used for Model serving, training, experimentation, and feature engineering. Have experience building infrastructure around machine learning pipelines leveraging modern tools with hands-on experience in modern programming languages such as Python ML and DS Experience: You have significant experience (ideally 3+ years) designing and implementing diverse machine learning models (e.g., regression, classification, tree-based ensembles) to solve real-world business problems, backed by a strong theoretical foundation in statistics and probability.

Application Development

Experience building new applications from scratch along with experience building quick proof of concepts (PoCs) for solving system design challenges using open-source tools.

Technical Evaluation

Experience in tech explorations to evaluate technologies for business problems and proposing solutions based on comprehensive pros and cons analysis.

System Analysis

Experience in deep-diving into existing systems for investigations and improvements.

Communication Skills

Fluency in English. Clarity in communication, both written and verbal.

Nice to Have

Experience with ML tools like Metaflow, Airflow, MLflow, Argo Workflows, and Cloud Notebooks, etc Prior knowledge of public cloud platforms (AWS, Google Cloud or equivalent). Experience with CI/CD pipelines and Infrastructure as Code (Terraform or equivalent). Knowledge of containerization and orchestration tools like Docker, Kubernetes, and Helm.