Top Machine Learning Jobs in Germany

The role involves technical leadership in ML for pricing and revenue, including model development, experimentation, and ensuring operational reliability with a focus on business impact.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design, build, and scale core infrastructure for machine learning models, manage production systems, and implement best practices in MLOps.

The role involves designing and implementing machine learning capabilities for autonomous vehicles, focusing on perception, prediction, and planning systems. Engineers will work on software development processes from model creation to deployment, leveraging established products.

The intern will focus on architecting systems for reliable task execution in machine learning agents, implementing verification layers, and optimizing performance.

Design, build, and operate end-to-end data engineering and MLOps solutions for real-time, low-latency ad personalization. Implement streaming pipelines (Flink, Spark), feature provisioning to Hopsworks, model serving and operationalization (Triton, SageMaker), CI/CD, monitoring, and automation. Collaborate with applied scientists and mentor engineers while owning cloud infrastructure (AWS, Databricks, Kubernetes) and production reliability.

Design, build, and operate end-to-end data engineering and MLOps solutions for real-time ad personalization. Develop high-throughput, low-latency streaming pipelines (Flink, Spark), provision features to Hopsworks, operationalize model serving, implement CI/CD, monitoring, and automation, and collaborate with applied scientists and product teams to optimize performance and reliability.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain ML applications, manage workflows, build data pipelines, and mentor junior engineers within a global team.