Job
- Level
- Erfahren
- Job Feld
- IT, Data, DevOps
- Anstellung
- Vollzeit
- Vertragsart
- Unbefristetes Dienstverhältnis
- Ort
- München
- Arbeitsmodell
- Onsite
Job Zusammenfassung
In dieser Position entwickelst du End-to-End Machine Learning Pipelines und baust große Datenpipelines, während du Modelle von der Experimentierung bis zur Bereitstellung für Fahrzeuge optimierst und überwachst.
Job Technologien
Deine Rolle im 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, 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.
Unsere Erwartungen an dich
Ausbildung
- University degree in Computer Science, Engineering, or a related field.
Qualifikationen
- Working knowledge of ML pipeline orchestration, experiment tracking, and hyperparameter optimization.
Erfahrung
- 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.
Unser Angebot
- 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.
Benefits
Work-Life-Integration
Gesundheit, Fitness & Fun
Themen mit denen du dich im Job beschäftigst
Job Standorte
Das ist dein Arbeitgeber
BMW AG
Weltweit führend in der Premium-Klasse: Ob Automobile, Motorräder oder Finanz- und Mobilitätsdienstleistungen - die Marken BMW, MINI, Rolls-Royce und BMW Motorrad stehen für höchste Qualität.
Description
- Unternehmenstyp
- Etablierte Firma
- Arbeitsmodell
- Hybrid, Onsite
- Branche
- Fahrzeugbau, Zulieferer, Industrie, Produktion
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MLOps Engineer - Implementation
- Ort
- München
- Arbeitsmodell
- Onsite
- Diversität
- Für alle Personen geeignet (m/w/d)
- Nur Englisch
- Nur Englisch erforderlich
