1345 English-speaking jobs in Bavaria

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. Drive the implementation of AI model architectures and leverage open-source AI frameworks.

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 system improvement. Develops ontologies and semantic models for the automotive AI domain. Architects agent memory systems and evaluates retrieval quality using various metrics.

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.

Develop and operate scalable AI platforms in cloud environments. Support the entire AI lifecycle, from data preparation to deployment. Automate infrastructure and workflows using DevOps and MLOps tools. Collaborate with data scientists and engineers to design and implement platform functionalities. Ensure platform quality, stability, and security.

Develop and deliver AI-based ADAS functions, including object detection, behavior prediction, and trajectory planning. Design and optimize humanlike driving behavior models. Own complete training, evaluation, and release cycles.

Develop and operate scalable AI platforms in cloud environments. Support the entire AI lifecycle, from data preparation to deployment. Automate infrastructure and workflows using DevOps and MLOps tools. Collaborate with data scientists and engineers to design and implement platform functionalities. Ensure platform quality, stability, and security.

Designs AI architecture for the AI-defined vehicle of tomorrow, integrating LLMs, VLAMs, End2End driving models, Foundation world models, and agent systems into ADAS architectures. Develops AI system strategy for ADAS, including model architectures, data flows, and integration concepts. Collaborates with expert teams to co-create cutting-edge ADAS products spanning L2 to L4 autonomous driving.

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 guidance to business units and development teams. Drives the implementation of scalable AI software platforms using state-of-the-art AI research.

Develop, implement, and operate AI/ML models for embedded automotive systems. Optimize AI models for performance, runtime, and memory efficiency on edge hardware. Collaborate with software and hardware specialists on hardware/software co-design.

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, 5+ years of application engineering experience, and strong Python and Java/Kotlin skills.

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, 5+ years of application engineering experience, and strong Python and Java/Kotlin skills.

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. Drive the implementation of AI model architectures and leverage open-source AI frameworks.

Develop and operate scalable AI platforms in cloud environments. Support the entire AI lifecycle, from data preparation to deployment. Automate infrastructure and workflows using DevOps and MLOps tools. Collaborate with data scientists and engineers to design and implement platform functionalities. Ensure platform quality, stability, and security.

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 system improvement. Develops ontologies and semantic models for the automotive AI domain. Architects agent memory systems and evaluates retrieval quality using various metrics.

Develop and operate scalable AI platforms in cloud environments. Support the entire AI lifecycle, from data preparation to deployment. Automate infrastructure and workflows using DevOps and MLOps tools. Collaborate with data scientists and engineers to design and implement platform functionalities. Ensure platform quality, stability, and security.

Senior Applied AI/ML Engineer develops and refines edge GenAI models for the BMW Intelligent Personal Assistant. Selects, adapts, and fine-tunes models for voice assistant and intelligence tasks. Implements compression strategies and collaborates with performance engineers on hardware-aware model variants.

Develop, implement, and operate AI/ML models for embedded automotive systems. Optimize AI models for performance, runtime, and memory efficiency on edge hardware. Collaborate with software and hardware specialists on hardware/software co-design.

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.

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. Execute full training and evaluation cycles, from data selection and scene analysis to error diagnosis and validation. Translate market needs, regulatory requirements, and user behavior into production-ready function increments.

Develop and deliver AI-based ADAS functions, including object detection, behavior prediction, and trajectory planning. Design and optimize humanlike driving behavior models. Own complete training, evaluation, and release cycles.