424 English-speaking jobs in Munich

Network Engineer responsible for the secure and stable operation of the company's network infrastructure. Experience with CISCO and Meraki network solutions is required.

Manage and support Microsoft 365 applications including Teams, SharePoint, OneDrive, Intune, and PowerApps. Provide 2nd-level support, including root cause analysis and preventive measures. Support Workplace Management, software distribution, and business applications.

Lead Backend Engineer will define and evolve backend systems architecture, ensuring scalability and security. Responsibilities include driving backend projects from concept to delivery, mentoring engineers, and leading improvements to monitoring and observability. Strong proficiency in Python, C++, Node.js, and backend frameworks is required, along with experience with AWS services and infrastructure as code.

Develop and optimize embedded machine learning models for connected vehicles. Own the full lifecycle of models from concept to production deployment. Apply state-of-the-art compression techniques and collaborate with software and hardware specialists.

Builds and maintains end-to-end ML pipelines, including data ingestion, training, evaluation, and deployment. Engineers petabyte-scale data pipelines for training models. Develops tooling for efficient data processing and integration with dataset management platforms. Manages experiment tracking, hyperparameter tuning, and model registry. Develops and maintains model compilation pipelines for in-vehicle hardware. Operates observability stacks for monitoring and alerting.

Develops end-to-end AI-based ADAS functions, including object detection, behavior prediction, and trajectory planning. Designs and optimizes humanlike driving behavior models using various techniques. Implements and executes complete training, evaluation, and release cycles for ADAS AI models.

The Deutsches Museum seeks an IT Systems Engineer / IT Specialist for System Integration to manage and modernize network infrastructure. Responsibilities include network design, operation, troubleshooting, and support, as well as transitioning to cloud technologies.

Designs end-to-end AI and data architectures for future vehicle generations, focusing on AI trends. Builds architectural concepts for diverse use cases, such as digital cockpits and driver interaction. Provides architectural guardrails to business units and development teams. Drives the implementation of scalable AI software platforms using state-of-the-art AI research.

As a Senior AI Platform Security Engineer, you will define architecture standards, security policies, and SLAs/SLOs for an AI Moderation Layer. You will lead the design of a multi-layered defense strategy to protect against AI threats and establish and improve the security lifecycle for LLMs/Agents. You will collaborate with legal, compliance, and data protection teams to ensure regulatory alignment for AI security. You will also mentor and guide an agile team of engineers and data scientists.

Design end-to-end AI and data architectures for vehicles, focusing on edge computing and diverse use cases. Build scalable AI software platforms and define interfaces for application development.

Define AI system strategy for ADAS, including model architectures, data flows, and integration concepts. Develop prototypes, evaluation pipelines, and data models, ensuring they meet real-world product requirements. Collaborate with expert teams to create cutting-edge ADAS products spanning L2 to L4 autonomous driving.

Designs and operates knowledge graphs and graph databases to capture domain knowledge and operational data. Builds context-engineering pipelines for LLM-based agents and implements feedback loops for continuous improvement. Develops ontologies and semantic models for the automotive AI domain.

Develop and industrialize end-to-end machine learning algorithms for ADAS functions. Design and improve human-like driving behavior using behavior models, reinforcement learning, and hybrid data- and model-driven approaches.

Senior Applied AI/ML Application Engineer will build, prototype, and deliver use cases leveraging edge device models for the BMW Intelligent Personal Assistant. Responsibilities include integrating models, prototyping GenAI experiences, collaborating with model engineering, and building evaluation tools. Requires a university degree in a related field, at least five years of application engineering experience, and strong Python and Java/Kotlin skills.

Senior Applied AI/ML Engineer will select and adapt base models for voice assistant and intelligence tasks. Develop training, fine-tuning, and evaluation pipelines to refine models. Implement compression strategies and collaborate with performance engineers on hardware-aware model variants.

Develop and operate scalable, secure, and high-performance AI platforms in cloud environments. Support the entire AI lifecycle, from data preparation to productive operation of machine learning and generative AI solutions. Automate infrastructure, deployments, and workflows using DevOps and MLOps approaches. Collaborate with data scientists, software engineers, and architects to design and implement platform functionalities. Ensure platform quality, stability, and security. Evaluate new technological trends in AI and contribute to the development of the AI landscape.

Designs, builds, and manages Kubernetes clusters on AWS EKS for autonomous driving AI/ML workloads. Provisions and orchestrates large-scale GPU clusters for distributed AI/ML training and experimentation. Implements and automates CI/CD pipelines for AI/ML workflows. Develops and maintains infrastructure components tailored to autonomous driving data. Collaborates with data scientists, AI/ML engineers, and cloud architects.

Validation Engineer will build and operate cloud-based validation environments for a multi-agent AI system. Responsibilities include designing AI-assisted exploration strategies, developing coverage models and metrics, establishing validation methodologies, and building observability tooling.

Develop and operate AI platforms for data pipelines, training, deployment, and inference. Optimize AI models and lead technical solutions for scalable AI platform features. Ensure reliability, performance, and cost-efficient operation of AI-based systems.

Act as a financial business partner for the Dairy Category, providing insights and driving data-driven decisions. Ensure accurate month-end closings, own the forecasting process, and support finance transformation.