Salary: 65.000 - 108.000 € per year Requirements: University degree in Computer Science, Computer/Electrical Engineering or related subjects. 5-8 years in ML platform or infrastructure engineering, with at least two years in a tech lead or architect role. Deep expertise in either AWS, Azure or Google cloud, ideally with multi-region or multi-account setups. Proven track record designing systems for PB-scale data and hundreds of concurrent training jobs as well as understanding of large vision models and challenges of compressing them for automotive-grade SoCs. Strong knowledge of Kubernetes platform design, GitOps, and infrastructure-as-code. Excellent communication skills to align ML researchers, embedded engineers, data teams, and executives. Familiarity with edge model compilation toolchains for Qualcomm (QNN, AIMET) and/or NVIDIA (TensorRT, Triton) and experience with automotive data at scale, such as MDF4, MCAP, ROS bags, and multi-sensor synchronisation. Responsibilities: Design the reference architecture for the ML platform end-to-end: data ingestion, PB-scale data lake, heterogeneous training clusters, model registry, and deployment-ready artefacts. Design the data-format backbone, setting standards for data flows, ingestion, cataloguing, transcoding, and partitioning at PB scale, integrated with dataset management tooling. Define the platform component topology and integration contracts for pipeline orchestration, experiment tracking, hyperparameter optimisation, dataset management, observability, and metadata. Establish model lifecycle governance, including experiment tracking, approval gates, validation criteria, and clear handoff contracts to deployment teams. Drive cost governance at PB scale, including accelerator spot strategies, S3 tiering, cross-AZ traffic reduction, and Kubernetes cluster right-sizing. Partner with Security, Legal, and Functional-Safety teams on ISO 26262, ISO 8800, and data-protection compliance. Technologies: AWS Architect Azure Backbone Cloud Embedded GitOps Kubernetes ROS Security AI Hardware Machine Learning More: At the BMW Group, located in Munich, we are a globally leading premium manufacturer of automobiles and motorcycles, and we provide premium financial and mobility services. We offer challenging projects to shape the future of mobility alongside a wide range of personal and professional development opportunities. Our compensation is attractive, fair, and performance-related, with benefits including high job security, annual special payments, flexible working hours, and discounted BMW & MINI conditions. We are excited to create innovative mobility solutions together. last updated 13 week of 2026