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Senior ML Ops Engineer - Architecture & Strategy

Germany, Munich · Job Posted March 21, 2026
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Job Description

We own the platform blueprint for our ML infrastructure: designing systems that integrate with a data mesh of domain-owned data products, leverage Qualcomm Cloud AI 100 and NVIDIA GPU clusters for training at petabyte scale and produce optimised model artefacts ready for deployment to vehicle hardware. We set technical direction, make build-vs-buy decisions, and ensure the platform scales to hundreds of engineers.

Job Responsibility

  • You 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
  • You design the data-format backbone, setting standards for data flows, ingestion, cataloguing, transcoding, and partitioning at PB scale, integrated with dataset management tooling
  • You define the platform component topology and integration contracts for pipeline orchestration, experiment tracking, hyperparameter optimisation, dataset management, observability, and metadata
  • You establish model lifecycle governance, including experiment tracking, approval gates, validation criteria, and clear handoff contracts to deployment teams
  • You drive cost governance at PB scale, including accelerator spot strategies, S3 tiering, cross-AZ traffic reduction, and Kubernetes cluster right-sizing
  • You partner with Security, Legal, and Functional-Safety teams on ISO 26262, ISO 8800, and data-protection compliance

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 the 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

What we offer

  • Challenging projects with which we shape the mobility of tomorrow together
  • Wide range of personal and professional development opportunities
  • Attractive, fair and performance-related remuneration
  • High level of job security
  • Annual special payments such as vacation pay, Christmas bonus, and profit sharing
  • Flexible working hours including six weeks annual leave and overtime compensation
  • Discounted BMW & MINI conditions
  • Many other benefits at bmw.jobs/benefits

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