MLOps Engineer - Implementation (f/m/x)

Referenznummer: 26784_182753

Standort: München, BY, DE, 80809

Unternehmen: BMW Group

INNOVATION IS IMAGINING WHAT NO ONE ELSE CAN.

At the BMW Group, everything begins with passion. It transforms a profession into a vocation. It drives us to continually reinvent mobility and bring innovative ideas to the roads. Enthusiasm for collaborative projects turns a team into a strong unit where every opinion is valued. It is only when expertise, highly professional processes, and enjoyment of work come together that we can shape the future collectively.

We build and operate the ML infrastructure that takes perception and vision models from experiment to production - across a data mesh of domain-owned datasets, through large-scale distributed training on Qualcomm Cloud AI 100 and NVIDIA GPU clusters, all the way to optimized, deployment-ready artefacts for resource-constrained hardware in the vehicle.

What awaits you?

• You build and maintain end-to-end ML pipelines using workflow orchestration tools: from data ingestion to distributed training, evaluation, model compilation, and deployment-ready artefacts.
• Furthermore, you engineer petabyte-scale data pipelines that consume domain datasets, transforming raw MDF4 (.mf4) and MCAP log files into training-ready formats.
• You build tooling for efficient parallel readers, signal extraction, synchronisation of multi-sensor streams, and integration with dataset management platforms for visual QA and curation.
• Also, you manage experiment tracking, hyperparameter tuning and model registry, enforcing reproducibility, lineage, and approval gates from experiment to production.
• You develop and maintain model compilation and optimisation pipelines targeting in-vehicle Qualcomm Snapdragon Ride chips and/or NVIDIA automotive SoCs.
• On top, you operate observability stacks, providing dashboards, data-drift alerts, pipeline SLOs, and log aggregation.

What should you bring along?

• 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 deploying and debugging workloads, writing 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) and automotive measurement data, such as MDF4 or MCAP.
• Comfortable with relational databases (e.g., PostgreSQL) for metadata stores and experience with dataset management tools, functional-safety awareness (ISO 26262), or AUTOSAR Adaptive.

Are you excited to shape the future of automotive AI infrastructure? Apply now!
Find out more about Artificial Intelligence at the BMW Group here.

Note: Please apply exclusively online via our career portal. Applications through other channels (especially email) cannot be considered.

What do 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

Earliest starting date: from now on

Type of employment: unlimited

Working hours: full-time

If you apply, the next stages of the recruiting process include an online test and then technical interviews with the hiring manager (either virtual or in person).

You can find helpful tips on your application and the application process here.

At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants. Learn more here.