Full-Time Machine Learning Engineer

Join our team of machine learning experts to bring cutting-edge causal inference models and algorithms into production at scale.

Collaborate closely with data scientists to deploy, optimize, and maintain models that drive performance marketing, portfolio optimization, and large-scale ad automation.

Build robust and scalable pipelines, ensuring these advanced solutions deliver actionable insights and measurable impact in real-world applications.

YOUR ROLE AT SIXT

1. Model Deployment and Optimization: Work with data scientists to implement and deploy causal inference models for online marketing optimization and automation —including A/B testing, difference-in-differences, propensity score matching, and double machine learning—in production environments.
2. Build and Maintain ML Pipelines: Design, develop, and maintain end-to-end pipelines for deploying machine learning models, ensuring scalability, reliability, and seamless integration with existing systems.
3. Cross-Functional Collaboration: Collaborate with data scientists, product managers, and software engineers to transform experimental models into fully operational systems that drive business outcomes.
4. Monitoring and Performance Tuning: Continuously monitor deployed models for performance, latency, and accuracy. Implement feedback loops and conduct regular updates to improve and adapt models to changing business needs.
5. Scalability and Automation: Automate repetitive tasks and build scalable infrastructure to handle large-scale data and high-throughput model serving.
6. Knowledge Sharing: Document best practices and deployment workflows. Communicate technical implementation details and insights to technical and non-technical stakeholders to foster collaboration and understanding.

YOUR SKILLS MATTER

You have strong Foundations in Machine Learning Engineering: Experience in building, deploying, and maintaining ML models in production, with a focus on ensuring robustness and scalability. You have proficiency in Python and production-oriented ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). You have experience with ML pipeline tools like MLflow, Airflow. You have strong understanding of REST APIs, microservices architecture, and containerization tools like Docker and Kubernetes. You are familiar with causal inference models and techniques, including A/B testing, difference-in-differences, and propensity score matching, is a plus.

You have Cloud and Big Data Experience: Hands-on experience with cloud platforms (AWS) and their ML/AI services. You are knowledgeable of distributed systems and big data technologies, such as Spark, Dask, or Polars, as well as multi-processing techniques for efficient offline learning and batch processing.

Collaboration and Communication Skills: Ability to work closely with data scientists to understand modeling requirements and translate them into scalable engineering solutions. Clear communication of technical concepts to cross-functional teams.

Problem-Solving Mindset: Proactive and growth-oriented, with a passion for addressing real-world problems through advanced engineering solutions.

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