Top Machine Learning Jobs in Germany

As Lead Systems Engineer, oversee integration of EO/IR sensing systems, manage technical suppliers, define requirements and ensure operational readiness.

As an Enterprise AI Adoption Manager, you will guide companies in adopting AI, manage enterprise accounts, drive AI transformation, and work closely with engineering and product teams to deliver value.

Lead the development of a real-time recommendation engine in Ad Tech, optimizing machine learning solutions for high availability and low latency while mentoring engineers and collaborating with cross-functional teams.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

Design and implement scalable machine learning systems, collaborate with product teams, and apply MLOps best practices in production environments.

The Principal Machine Learning Architect will design and implement data-centric ML solutions, ensuring high-quality ML model development and deployment strategies for a leading ad tech company.

As a Machine Learning Engineer, you'll support the development and deployment of AI/ML models, work with complex data, and collaborate on project objectives.

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.

The Senior Data Scientist drives AI platform development, establishes standards, partners with engineering teams, mentors, and ensures responsible AI adoption across Awin.

As a Full-Stack ML Engineer, you will develop backend services, data pipelines, and customer-facing UIs while leveraging ML. You'll manage the software lifecycle and write code in Python and Typescript.

Lead the development of robot policies and task planning. Collaborate with cross-functional teams to create real-world robotics solutions, incorporating AI technologies and MLOps practices.

The Senior Machine Learning Engineer will design evaluation frameworks, diagnose model performance, develop ML systems, and mentor junior engineers while collaborating across teams.

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

Lead the development of high-fidelity simulations and validation workflows for robotic systems, focusing on sensor simulation and mechanical modeling to ensure reliable performance in real-world and virtual environments.

Design and build simulation environments for robotics research, implement RL algorithms, and evaluate policies for stability and transfer potential. Collaborate with teams and document results.

Lead development of AI/ML systems for real-time applications across various industries. Responsibilities include data analysis, system architecture, developing models, mentoring, and collaborating with engineers.

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

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.

Design and develop perception algorithms for autonomous vehicles, focusing on 3D detection, sensor fusion, and deep learning optimization. Collaborate with teams to ensure production-ready solutions and stay current with advancements in computer vision and robotics.

The Expert Machine Learning Optimization Engineer will optimize machine learning models for self-driving vehicles, focusing on performance, robustness, and real-time deployment on automotive hardware.

Build scalable, searchable data infrastructure and ML pipelines for autonomous driving systems while collaborating with engineering teams to ensure data quality and integrity.