MLOps Engineer

About the Role

At the BMW Group, passion drives innovation in mobility. We are seeking an MLOps Engineer to build and operate ML infrastructure, taking perception and vision models from experiment to production. This role involves working with a data mesh, large-scale distributed training on advanced hardware, and deploying optimized artifacts for resource-constrained in-vehicle systems.

What You'll Do

• Build and maintain end-to-end ML pipelines, from data ingestion to deployment-ready artifacts, using workflow orchestration tools.
• Engineer petabyte-scale data pipelines for automotive measurement data (MDF4, MCAP), transforming raw logs into training-ready formats.
• Develop tooling for efficient data processing, including parallel readers, signal synchronization, and integration with dataset management platforms.
• Manage experiment tracking, hyperparameter tuning, and model registry, ensuring reproducibility and approval gates.
• Develop and maintain model compilation and optimization pipelines for in-vehicle Qualcomm Snapdragon Ride chips and NVIDIA automotive SoCs.
• Operate observability stacks, providing dashboards, data-drift alerts, pipeline SLOs, and log aggregation.

What You Bring

• University degree in Computer Science, Engineering, or a related field.
• 3–5 years of hands-on ML infrastructure or MLOps experience.
• Strong Python skills; experience with hermetic build systems (e.g., Bazel) is a plus.
• Production Kubernetes experience, including deployment, debugging, Helm charts, and managing accelerator node pools.
• Working knowledge of ML pipeline orchestration, experiment tracking, and hyperparameter optimization.
• Hands-on experience with infrastructure-as-code for AWS (e.g., Terraform).
• Experience with automotive measurement data (MDF4 or MCAP), relational databases (e.g., PostgreSQL), and dataset management tools.
• Functional-safety awareness (ISO 26262) or AUTOSAR Adaptive knowledge is beneficial.

What We Offer

• Opportunity to shape the future of automotive AI infrastructure.
• Challenging projects contributing to the mobility of tomorrow.
• Wide range of personal and professional development opportunities.
• Attractive, fair, and performance-related remuneration.
• High level of job security.
• Annual special payments (vacation pay, Christmas bonus, profit sharing).
• Flexible working hours, six weeks annual leave, and overtime compensation.
• Discounted BMW & MINI conditions.
• Access to more benefits at bmw.jobs/benefits

Key skills/competency

• MLOps Engineer
• Machine Learning
• Python
• Kubernetes
• Data Pipelines
• AWS
• Terraform
• ML Infrastructure
• Automotive AI
• Model Deployment