Quantum-Systems GmbH
(Senior) ML Platform Engineer
Als (Senior) ML Platform Engineer bei Quantum-Systems in Gilching entwickelst und betreibst du die Plattform für den gesamten ML-Lebenszyklus, von Datenverarbeitung und Annotation bis zu verteiltem Training und Modellvalidierung. Die Rolle ermöglicht ML-Teams die effiziente Entwicklung von Autonomi…
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Job description
As a Senior Machine Learning Platform Engineer (m/f/d), you will design and build the platform that turns large-scale UxV and sensor data into validated, reproducible machine learning models.
You will own and improve the end-to-end ML lifecycle—from data processing, selection, and annotation through distributed model training, experiment tracking and validation. Your work will enable ML engineers to develop autonomy and perception capabilities efficiently, reliably, and at scale.
Beyond conventional platform engineering, you will also apply modern AI models to our internal data workflows. This may include semantic dataset search, automated metadata generation, data curation, and auto-labeling using foundation and multimodal models.
By creating the foundations on which our AI teams build and validate models, you will directly contribute to Quantum Systems’ next generation of autonomous UxV solutions.
What is your Day to Day Mission:
- Design, implement, and operate scalable ML and data pipelines covering ingestion, dataset creation, training, and model validation.
- Build a reliable ML platform for reproducible experimentation and training across local, on-premises, and cloud GPU infrastructure.
- Develop efficient systems for large-scale multimodal datasets, including imagery, LiDAR, odometry, ground truth, and simulation data.
- Establish dataset and model versioning, metadata management, quality checks, and traceability from raw sensor data to validated model artifacts.
- Build and optimize distributed training workflows, job scheduling, GPU resource allocation, caching, and high-throughput data access.
- Develop AI-powered tools for internal data engineering, including semantic data search, metadata enrichment, automatic annotation, and quality assessment using foundation or multimodal models.
- Improve platform reliability, observability, security, cost efficiency, and developer experience.
- Collaborate with ML, data, and engineering teams to translate their technical needs into scalable platform capabilities.
- Mentor engineers, contribute to architectural decisions, and establish engineering standards for production-grade ML systems.