Job
- Level
- Experienced
- Job Field
- IT, Data, DevOps
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Munich
- Working Model
- Onsite
Job Summary
In this role, you will develop end-to-end machine learning pipelines and build large data pipelines while optimizing and monitoring models from experimentation to deployment for vehicles.
Job Technologies
Your role in the team
- 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.
- 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, synchronization 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.
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Our expectations of you
Education
- University degree in Computer Science, Engineering, or a related field.
Qualifications
- Working knowledge of ML pipeline orchestration, experiment tracking, and hyperparameter optimization.
Experience
- 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.
- 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.
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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.
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Benefits
Work-Life-Integration
Health, Fitness & Fun
Topics that you deal with on the job
Job Locations
This is your employer
BMW AG
Our world-leading premium automotive brands BMW, MINI, Rolls-Royce and our motorcycles, along with our comprehensive range of high-quality financial and mobility services make us a unique provider.
Description
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Vehicle Manufacturing, Supplier, Industry, Production
Dev Reviews
by devworkplaces.com
Total
(2 Reviews)3.1
Career Growth
3.1Engineering
2.3Culture
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4.1
MLOps Engineer - Implementation
- Location
- Munich
- Working Model
- Onsite
- Diversity
- Open for all genders
- English Only
- English only required
