56 engineer English-speaking jobs in Munich

Furthermore, you engineer petabyte-scale data pipelines that consume domain datasets, transforming raw

Technical AI Engineer responsible for complex, cross-functional AI initiatives from requirements analysis to implementation. Designs and develops scalable AI systems, focusing on Generative AI, and manages AI-related projects. Collaborates with internal and external partners.

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

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 learning paradigms. Implements and executes complete training, evaluation, and release cycles, ensuring data selection, scene mining, and KPI-based validation.

Designs, builds, and operates GPU-centric AI infrastructure across on-prem and cloud environments. Focuses on performance, scalability, and efficiency of high-performance compute environments for distributed training and optimized model execution. Develops and operates core infrastructure components such as scheduling and resource management systems. Builds and maintains automated, reproducible infrastructure using modern tooling. Provides reliable and scalable infrastructure for BMW-specific AI use cases.

Develop and deploy domain-specific foundation models for the automotive value chain. Apply machine learning to 3D geometries, including CAD files and simulations. Benchmark methods for understanding relationships between geometries, textual descriptions, and physical properties. Collaborate with a multidisciplinary team and external partners.

Senior Applied AI/ML Engineer develops and refines edge GenAI models for the BMW Intelligent Personal Assistant. Responsibilities include selecting and adapting base models, creating training pipelines, implementing compression strategies, and collaborating with performance engineers. Requires a university degree in a related field, experience in applied machine learning, proficiency in PyTorch and Transformers, and expertise in model compression and fine-tuning.

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.

Designs data pipelines for ingestion, curation, and versioning of data. Implements reproducible training and fine-tuning builds for GenAI edge device models. Sets up experiment tracking and artifact registries. Builds evaluation suites covering KPIs such as latency, memory, hallucination, safety, and harms. Optimizes dataset construction for small LLMs. Builds and operates data pipelines and provides self-serve tools.

Senior Machine Learning Engineer will evaluate and select GenAI models to enhance simulation performance. Develop and operate a scalable training and MLOps infrastructure, including distributed training, automated evaluation pipelines, and optimized cloud/on-prem compute usage. Optimize the GPU toolchain, scheduling, profiling, and resource allocation. Contribute to the simulation of driver assistance systems and evaluate simulation quality.

Integrate and prototype GenAI & ML models for the BMW Intelligent Personal Assistant. Develop new in-car intelligence features, collaborating with model engineering and UX teams. Requires a computer science or engineering degree and 5+ years of application engineering experience.

Define core semantic ontologies and context data models for autonomous driving and real-time systems. Drive the implementation of advanced data infrastructure functions and design a multi-level memory concept for vehicles. Orchestrate the integration of ML, GenAI, and symbolic reasoning rules to curate semantic context. Develop the data model strategy for fine-tuning SLMs and VLMs for agentic applications.

Designs and builds high-performance search and query pipelines over petabyte-scale data lakes, enabling rapid dataset assembly and model evaluation. Builds and operates indexing and cataloguing systems for automotive sensor data, including metadata extraction, signal-level indexing, and embedding-based similarity search.

Designs, builds, and operates GPU-centric AI infrastructure (especially NVIDIA) across on-premise and cloud environments. Requires strong hands-on experience with GPU systems, including drivers, CUDA, and performance optimization. Proficiency in Docker, Kubernetes, Terraform, Ansible, CI/CD, and Python for infrastructure and system-level tooling.

Designs, builds, and operates GPU-centric AI infrastructure (especially NVIDIA) across on-premise and cloud environments. Requires strong hands-on experience with GPU systems, including drivers, CUDA, and performance optimization. Proficiency in Docker, Kubernetes, Terraform, Ansible, CI/CD, and Python for infrastructure and system-level tooling.

Define core semantic ontologies and context data models for autonomous driving and real-time systems. Drive the implementation of advanced data infrastructure functions and design a multi-level memory concept for vehicles. Orchestrate the integration of ML, GenAI, and symbolic reasoning rules to curate semantic context. Develop the data model strategy for fine-tuning SLMs and VLMs for agentic applications.

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.

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