1191 English-speaking jobs in Bavaria

Senior AI Platform Security Engineer responsible for defining architecture standards, security policies, and ensuring global scalability for AI platform services. Leads the design of a multi-layered defense strategy to protect against AI threats and establishes the security lifecycle for LLMs/Agents. Collaborates with legal, compliance, and data protection teams to ensure regulatory alignment. Mentors and guides a team of engineers and data scientists. Requires 8+ years of experience in software or platform security and architecture in cloud environments, deep expertise in LLM and agent security, and strong leadership in security lifecycle and compliance.

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, 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.

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.

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.

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.

Builds and maintains end-to-end ML pipelines, including data ingestion, training, and deployment. Engineers petabyte-scale data pipelines and develops tooling for efficient data processing. 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.

Builds and maintains end-to-end ML pipelines, including data ingestion, training, and deployment. Engineers petabyte-scale data pipelines and develops tooling for efficient data processing. 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.

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.

Design data pipelines for AI model training and finetuning. Implement experiment tracking and build evaluation suites for edge AI products. Optimize datasets for small LLMs and build self-serve data tools.

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.

Design data pipelines for AI model training and finetuning. Implement experiment tracking and build evaluation suites for edge AI products. Optimize datasets for small LLMs and build self-serve data tools.

Develop and operate AI platforms for data pipelines, training, deployment, and inference. Optimize AI models and ensure reliability, performance, and cost-efficient operation of AI-based systems. Translate stakeholder requirements into scalable AI platform features.

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 and operate AI platforms for data pipelines, training, deployment, and inference. Optimize AI models and ensure reliability, performance, and cost-efficient operation of AI-based systems. Translate stakeholder requirements into scalable AI platform features.

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.

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, 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, 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 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.