191 germany English-speaking jobs in Munich

Builds and maintains end-to-end ML pipelines, including data ingestion, training, evaluation, and deployment. Engineers petabyte-scale data pipelines, transforming raw automotive data into training formats. Develops and maintains model compilation pipelines for in-vehicle hardware. Operates observability stacks, providing dashboards, alerts, and log aggregation.

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.

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.

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.

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.

Develop and maintain an AI-supported software development ecosystem, including AI coding assistants and tools. Optimize the software development lifecycle through AI integration and evaluate new AI technologies. Collaborate with development teams to promote AI adoption and share knowledge through workshops and training.

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, 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.

The Regional Delivery Assurance Manager ensures projects follow defined delivery assurance processes, from risk management in the presales phase to project delivery. Responsibilities include implementing and executing delivery assurance processes, conducting risk reviews, and managing risk mitigation activities. The role requires 5+ years of experience in SAP project delivery, 3+ years in risk management and quality assurance, strong communication and stakeholder management skills, and fluency in English. Willingness to travel 20-40% is required.

Design end-to-end AI and data architectures for vehicles, focusing on edge computing and diverse use cases. Build scalable AI software platforms using the latest technologies and in-house stacks. Define interfaces between platforms and applications, ensuring new requirements are integrated into AI and data platforms.

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. Co-creates and prioritizes AI use cases, assesses feasibility and value, and translates them into system and hardware requirements. Evaluates the latest AI developments and implements them where they add value.

Develop and optimize embedded machine learning models for connected vehicles. Own the full lifecycle of models, from concept to production deployment. Collaborate with software, machine learning, and semiconductor specialists. Apply state-of-the-art compression techniques and optimize for runtime, compute, and memory efficiency.

Designs the end-to-end reference architecture for the ML platform, including data ingestion, data lake, training clusters, model registry, and deployment artifacts. Defines data format standards and platform component topology. Establishes model lifecycle governance and drives cost governance at petabyte scale.

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 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.

Design, develop, train, validate, and optimize machine learning models for automotive use cases. Work with large-scale data to create robust models and ensure performance, robustness, and explainability. Deploy models into production systems and collaborate with teams to establish best practices for AI development.

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.