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.

You will 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

Strong Foundations in Machine Learning Engineering, Proficiency in Python and production-oriented ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn), Experience with ML pipeline tools like MLflow, Airflow, strong understanding of REST APIs, microservices architecture, and containerization tools like Docker and Kubernetes.

Cloud and Big Data Experience: Hands-on experience with cloud platforms (AWS) and their ML/AI services. Knowledge 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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