Top Machine Learning Jobs in Munich

Lead developer-facing work on advanced real-time rendering and neural graphics: optimize GPU/CPU performance, develop and tune neural rendering and AI models, collaborate with AAA studios and NVIDIA architecture/driver teams, build demos, publish and present research.

As a Senior Forward Deployed Engineer, you'll deliver AI solutions, engage with customers, and lead project lifecycles, including deployment and integration.

The Senior Product Manager will own and drive the development of Kraken's Algorithmic Execution & Strategy Engine, focusing on execution algorithms, API specifications, and client engagement to optimize trading strategies.

As a C++ Engineer, you will develop machine learning features, integrate models, maintain SDKs, and collaborate across teams for software performance and enhancements.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

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

The Senior AI Engineer will design and develop intelligent conversational systems for recruiting, focusing on agentic workflows, ML/NLP products, and system integration.

The Functional Safety Manager will lead ISO 26262 certification efforts for SoC products, define safety strategy, run risk assessments, and manage safety documentation while collaborating with HW/FW/SW teams.

As a Partner Solutions Architect, you will build and maintain technical relationships with partners, support solution development, and enable AI practices to drive revenue.

The Data Scientist will solve real-world problems utilizing data science techniques and build end-to-end data pipelines, collaborating with multiple teams to deliver solutions.

As a Machine Learning Engineer, you'll develop machine learning solutions for medical data analysis, particularly focusing on surgical video processing. You will engage in all phases of the ML development lifecycle to enhance surgical procedures through intelligent software components.

As a Senior ML Researcher at JetBrains, you will train LLMs for software engineering agents, design experiments, and mentor junior colleagues.

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

Intern will explore collaborative learning and differential privacy while developing efficient systems for large ML models, aiming for academic publication.

The intern will contribute to MLOps, design AI/ML systems, implement research ideas, and work on various optimization models, while gaining hands-on experience.

Lead the development of fraud prevention products, collaborating with technology and risk teams, analyzing trends, and ensuring compliance with regulations.

The AI Researcher/Engineer will develop AI agents to autonomously conduct scientific discovery, leveraging AI systems and scientific computing in materials research.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Design, train, evaluate, and optimize machine learning models for document intelligence and GenAI. Collaborate across teams to develop AI systems for document understanding and automation.

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.

Lead the LLM team to architect, train, and deploy AI models, optimizing real-time user experiences and creating advanced moderation systems.

The Applied Machine Learning Scientist will innovate and implement ML algorithms to enhance advertising performance, collaborate with engineers, and prototype in data-driven models.

Internship focused on enhancing ML/LLMOps and AI agent infrastructure within AWS: evaluating LLM frameworks, building RAG pipelines, automating model evaluation/deployment, documenting findings, and contributing to ML/data pipelines.

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.